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Identifying conservation priorities and assessing impacts and trade-offs of
potential future development in the Lower Hunter Valley in New South Wales
A report by the NERP Environmental Decisions Hub
Heini Kujala, Amy L. Whitehead and Brendan A. WintleThe University of Melbourne
The Environmental Decisions Hub is supported through funding from the Australian Government’s National Environmental Research Program www.environment.gov.au/nerp and involves researchers from the University of Western Australia (UWA), The University of Melbourne (UM), RMIT University (RMIT), The Australian National University (ANU), The University of Queensland (UQ) and CSIRO .
Identifying conservation priorities and assessing impacts and trade-offs of potential future development in the Lower Hunter Valley in New South Wales.
ISBN: 978-07340-5140-0 (PDF)Hub Research Theme: 4.4 Regional Sustainability Plans (Hunter)Enquiries to: [email protected]
© The University of Melbourne This work is copyright. It may be produced in whole or in part for study or training purposes subject to the inclusion of an acknowledgement of the source. It is not intended for commercial sale or use. Reproduction for other purposes other than those listed above requires the written permission from the authors.
For permission to reproduce any part of this document, please approach the authors.
Please cite the report as follows:
Kujala H, Whitehead AL & Wintle BA (2015) Identifying conservation priorities and assessing impacts and trade-offs of potential future development in the Lower Hunter Valley in New South Wales. The University of Melbourne, Melbourne, Victoria. Pp. 100
Purpose of the ReportThis report describes the framework and tools used to identify areas of high conservation priority in the Lower Hunter, and to assess the individual and cumulative impacts of potential future development scenarios. The report is an output of the Environmental Decisions Hub.
About the AuthorsDr Heini Kujala is a Research Fellow specialised in the use of spatial conservation tools to explore protected area effectiveness and resource optimisation, and building conservation recilience under climate change. Dr Amy Whitehead is an ecological modeller with interests in conservation planning and management across a wide range of taxa and ecosystems. Associate Professor Brendan Wintle is the deputy director of the Environmental Decisions Hub and specialises in uncertainty and environmental decision making.
AcknowledgementsWe thank Ross Rowe, Dave Osborne, Huw Morgan, Jeremy Groves and Leanna Hayes (Department of the Environment); Meredith Laing, Ellen Saxon, Mary Greenwood, Bradley Nolan and Eva Twarkowski (HCCREMS); Paul Keighley, Amanda Wetzel and Dylan Maede (NSW Department of Planning and Environment); Mark Cameron, Sharon Molloy, Lucas Grenadier and David Paull (NSW Office of Environment and Heritage) for data, support and comments to this project.
This independent research is contributing to regional sustainability planning in the Lower Hunter Region, jointly undertaken by the Australian Government and the Government of NSW. The research was funded by the Australian Government through the Sustainable Regional Development Program and the National Environmental Research Program (NERP), which supports science that informs environmental policy and decision making. The report is an output from the Environmental Decisions Hub.
Table of Contents
Executive Summary..................................................................................................................1
Description of the work...............................................................................................................1
Points to consider when interpreting the results........................................................................2
Key findings.................................................................................................................................3
Conservation priorities and current protection of biodiversity in the LHSA region.................3
Potential impacts of development on biodiversity in the LHSA region...................................5
Recommendations........................................................................Error! Bookmark not defined.
Glossary of Terms.....................................................................................................................9
Description of threat categories.............................................................................................10
Frequently Used Abbreviations...............................................................................................11
Chapter 1 Introduction...........................................................................................................12
1.1. Purpose of the research project.........................................................................................12
1.2. Outline of the report..........................................................................................................13
1.3. Systematic conservation planning and spatial prioritisation – planning strategically for biodiversity........................................................................................................................14
1.4. Regional Sustainability Planning and Strategic Assessment in Hunter Valley.....................15
1.5. Study area...........................................................................................................................16
1.6. Framework for mapping biodiversity patterns and prioritising options under development scenarios...........................................................................................................................18
1.7. Stakeholder and expert engagement.................................................................................19
1.8. Previous conservation priorities in the Lower Hunter Region............................................21
Chapter 2 Biodiversity features included in the analysis..........................................................24
2.1. Flora and fauna species......................................................................................................24
2.1.1. Species data: point occurrences and modelled distributions......................................24
2.1.2. Additional species layers.............................................................................................25
2.2. Ecological Communities......................................................................................................27
2.2.1 Nationally-listed Ecological Communities.....................................................................27
2.2.2 State-listed Endangered Ecological Communities.........................................................27
Chapter 3 Modelling species potential distribution patterns...................................................29
3.1 Modelling the distribution of threatened species...............................................................29
3.2. Species distribution modelling............................................................................................29
3.2.3. Sampling bias layers....................................................................................................29
3.1.4. Environmental variables..............................................................................................30
3.2. Species distribution modelling............................................................................................31
3.2.1. Modelling species distributions using MaxEnt.............................................................31
3.2.2 Modelling EEC distributions using boosted regression trees........................................33
3.2.3. Model selection...........................................................................................................33
3.3. Results................................................................................................................................34
3.3.1. Model performance.....................................................................................................34
3.3.2. Regional distribution patterns and uncertainty...........................................................37
3.4. Discussion...........................................................................................................................38
3.4.1. Overview.....................................................................................................................38
3.4.2. Limitations and sources of uncertainty.......................................................................38
Chapter 4 Spatial conservation prioritisation of the Lower Hunter Region..............................41
4.1. Prioritising areas for conservation – the algorithm............................................................41
4.2. Features used in the prioritisation......................................................................................44
4.3. Weighting of features.........................................................................................................44
4.4. Protected Areas..................................................................................................................46
4.5. Results................................................................................................................................48
4.5.1. Conservation priorities within the LHSA region...........................................................48
4.5.2. Representation of features within the top priority sites.............................................53
4.5.3. Representativeness of the existing reserve network: gaps and opportunities............59
4.6. Discussion...........................................................................................................................63
4.6.1. Limitations and sources of uncertainty.......................................................................65
Chapter 5 Identification and assessment of potential development risks................................67
5.1. Development scenarios .....................................................................................................67
Scenario 1: Urban development............................................................................................68
Scenario2: Hunter Expressway..............................................................................................71
Scenario 3: Important Agricultural Lands..............................................................................71
Scenario 4: Cumulative impact of all urban, rural, infrastructure and agriculture development...............................................................................................................72
Scenario 5: Current mining titles and applications................................................................72
5.2. Analysis setup for assessing impacts of development........................................................75
5.3. Results................................................................................................................................75
Scenario 1: Urban development............................................................................................75
Scenario 2: Hunter Expressway.............................................................................................77
Scenario 3: Important Agricultural Lands..............................................................................81
Scenario 4: Cumulative impact of all urban, rural, infrastructure and agriculture development...............................................................................................................82
Scenario 5: Current mining titles and applications................................................................84
5.4. Discussion...........................................................................................................................86
5.4.1. Overview: Impacts of potential development on biodiversity in the LHSA region......86
5.4.2. Limitations and sources of uncertainty.......................................................................88
Chapter
6 Synthesis and future directions...........................................................................................90
6.1 Summary.............................................................................................................................90
6.1.1. Conservation priorities and current protection of biodiversity in the LHSA region.....90
6.1.2. Impacts of potential development on biodiversity in the LHSA region........................91
6.2. Discussion and future directions........................................................................................92
References.............................................................................................................................95
List of Appendices................................................................................................................100
Executive Summary
Description of the work
The aim of this work is to identify potential opportunities and priority areas for conservation,
and to estimate the impacts and ecological trade-offs of planned and proposed development
scenarios within the Lower Hunter Strategic Assessment (LHSA) region. The Lower Hunter Strategic
Assessment (LHSA) will be undertaken within the local government areas of Newcastle, Maitland,
Cessnock, Lake Macquarie and Port Stephens, collectively referred to as the ‘Lower Hunter’ in this
document. The Lower Hunter is located 160 km north of Sydney and extends over 4,290 km2 along
the east coast of Australia within the State of New South Wales (NSW; Figure 1). Using publically
available data, we map the distribution of 721 biodiversity features, including threatened flora and
fauna, Matters of National Environmental Significance (MNES), and important biodiversity habitats,
such as endangered ecological communities (EECs). These maps, which were based on a combination
of species distribution models (SDMs) and mapped known occurrences, were then combined using
systematic conservation planning and spatial prioritisation tools to identify areas of high
conservation priority within extant native vegetation in the LHSA region (~280,149 ha, approximately
65% of the LHSA region; Figure 1). We report how well these high priority conservation areas are
currently protected by national parks, nature reserves, heritage sites and other land tenures that
offer varying levels of protection. Assessing how well the current protected area network protects
biodiversity features within the LHSA region allows us to identify gaps in protection for MNES species
and communities, and opportunities for improving protection. Finally, we investigate how the areas
of high conservation priority are likely to be affected under five potential urban, infrastructure,
agriculture and mining development scenarios provided by Hunter & Central Coast Regional
Environmental Management Strategy (HCCREMS) and the NSW Department of Planning and
Environment (DPE) (Table A). We assess the potential biodiversity impacts of these development
scenarios by estimating the proportion of features’ current distributions within LSA region likely to
be lost to development and by identifying those species that are most strongly impacted. We also
report the proportion of high priority conservation areas (across all features) and currently
protected areas within the LHSA region at risk of being cleared in each scenario.
This report is designed to provide guidance on how spatial conservation planning tools can be
used to identify conservation opportunities and potential biodiversity losses due to development
within the LHSA region. It does not examine whether the resultant changes to biodiversity
distributions are adequate for the long-term persistence of populations, or whether development
scenarios (and potential losses) are acceptable under State and Federal environmental legislation. It
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also does not consider the implications of conservation or development activities outside the LHSA
boundary nor other potential threatening processes.
Points to consider when interpreting the results
The outputs from the analysis are only as good as the input data. Poor quality (i.e. inaccurate
species data) or missing data may result in the identification of conservation priorities or
potential development impacts that do not align with the real world. Our analysis was based
on the best available data at the time of the analysis but verification of key results is
recommended.
The high priority conservation areas identified in this report are important at the scale of the
LHSA region for the 721 species and communities included in the analysis. The relative
importance of these areas would likely change if the analysis included a different suite of
species or was undertaken at a different scale. However, we note that the approach used in
this work is flexible in the sense that the results could be re-produced for any combination of
biodiversity features and/or development scenarios.
The currently zoned areas in the urban development scenario assessed in this report were
based on spatial data released by DPE on February 15th 2015. It should be noted that these
data may be subject to future small scale refinements. Consequently the predicted impacts
on biodiversity reported here may differ from those expected under future refinements to
the development footprints.
The exact impact values for each scenario assume that all areas marked for development will
no longer accommodate biodiversity values. For the most part, this is a reasonable
assumption. However, there may be some impacted areas that maintain key habitat
features, allowing MNES or other values to persist. Further local scale avoidance and
mitigation could potentially reduce the impacts to biodiversity values (and increase the
conservation opportunities) to some degree, however these can only be assessed on a case-
by-case basis.
Our analysis allows us to assess the extent of potential impacts in terms of loss of high
priority conservation areas and the distributions of individual species and communities.
However, we are not able to say, based on the current analysis, whether the remaining
habitats are sufficient to support species persistence or what are the indirect impacts of
development through, for example, noise and pollution. This means that despite the
potential coarseness and errors in the footprint estimates, our approach is very likely to be a
conservative estimate of the full-scale ecological impacts of the assessed development
plans. To understand how much habitat loss may result in severe population declines and an
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increase in the risk of local extinction, or how much protection is enough to ensure
persistence, further analyses, such as species-specific population viability analyses (PVAs)
will be required.
Key findings
Conservation priorities and current protection of biodiversity in the LHSA region
The spatial analysis identified large areas within the LHSA region that are highly important
for the conservation of the biodiversity features included in this assessment. These high
priority conservation areas (considered here to be the most important 30% of the landscape
in terms of their biodiversity value) are distributed across the entire region, with important
areas in the localities of North Rothbury, Polkolbin, Abermain, Sawyers Gully, Pelaw Main
(near Kurri Kurri), Yengo National Park, Watagans National Park, Heaton State Forest, Anna
Bay, Koragang, and Tomago. (Box A). In many cases, these high priority conservation areas
are small fragments of remnant habitat that may contain the last known occurrences of
some biodiversity features. On average, the identified high priority conservation areas cover
50% of the LHSA distributions of all 721 species and communities included in this assessment
and 68% of the LHSA distributions of MNES features.
Thirty-three percent (92,762 ha) of the remnant vegetation within the LHSA region is
currently protected under a variety of tenures with varying protection status. Of these 28%
(78,239 ha) is protected by mechanisms of high legislative security (Level 1 protected areas:
e.g. nature reserves, national parks, conservation parks etc.). While Level 2 (including e.g.,
protected locations within State Forests, Ramsar wetlands and SEPP areas) and Level 3 (e.g.,
wildlife refuges and registered property agreements) protected areas are quite small in size,
covering only 4.5% and 0.7% of remnant vegetation, respectively, they currently protect
biodiversity features that are not represented in the most secure protected areas.
On average, 29% of all biodiversity features and 37% of MNES features LHSA distributions lie
within the most secure Level 1 protected areas. However, there is substantial variation
between species and communities, with 67 features (including seven MNES features)
currently having no protection within the LHSA region and 79 biodiversity features (11
MNES) being protected only by the less secure Level 2 and Level 3 protected areas.
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Box A. The high priority conservation areas identified within the LHSA region are based on the best 30% of the spatial prioritisation for the 721 species and communities considered in this analysis. The inset boxes highlight example areas of priority sites across the LHSA region. While many of these areas are currently protected, there exist significant opportunities to make conservation gains by strategically protecting areas that are currently unprotected or protected by insecure legislative mechanisms. Inset boxes: 1) Yengo National Park; 2) Watagans National Park and Heaton State Forest; 3) Luskintyre and Martinvale; 4) Anna Bay and One Mile.
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Of the identified high priority conservation areas over 70% are currently either unprotected (63%) or
protected by only the intermediate and low security protected areas (7.6%). Therefore, significant
conservation gains could be made by strengthening the level of protection of these sites, particularly
where they host currently poorly protected features and are under threat from future development.
Potential impacts of development on biodiversity in the LHSA region
We estimated the potential impacts of five development scenarios: 1) potential urban
expansion within LHSA, ranging from currently zoned areas to proposed investigation areas;
2) recent construction of the Hunter Expressway; 3) potential expansion of intensive
agriculture on areas of high agricultural value; 4) the cumulative impact of scenarios 1-3 and;
5) a hypothetical mining expansion scenario based on information on current mining titles
and applications (Table A).
In general, we did not find MNES to be more highly or less impacted by the potential
development in any of the scenarios (Table A).
The cumulative impact of all urban, infrastructure and agriculture development in LHSA
region might lead to the loss of up to 38,000 ha (14%) of the region’s native vegetation,
when assuming that all vegetation within the development sites is cleared. As a result, 17%
of biodiversity features LHSA distributions might be lost on average, but the impacts vary
greatly between biodiversity features. Some 32 biodiversity features (four MNES) may be at
risk of losing more than half of their known occurrences within the region, with 19 (two
MNES) of these potentially at risk of losing all their known occurrences. The full
development of these sites would also clear 17% of the identified high priority conservation
areas and 2.2% of currently protected areas. Significant conflicts, where potential
development overlaps with areas identified as within the most important 5% of the entire
region, were found especially around Branxton-Rothbury (including Huntlee), Kurri Kurri,
Rutherforth-Largs, Tomago-Heatherbrae and Southern Lake Macquarie (Box B).
The potentially most highly impacted features are predominantly vagrant or rare species and
communities in the LHSA region, with orchids forming the single largest taxonomic group
within them. However, they also include the vulnerable MNES Grevillea shiressii and another
six state-listed threatened features.
Of the scenarios contributing to the cumulative development, the greatest biodiversity
threat is posed by already zoned areas which still have native vegetation but have been
zoned for land use types that are likely to lead to extensive clearing. The clearing of these
areas would result in loss of up to 14,000 ha of native vegetation and, on average, an 8.2%
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loss in features LHSA distributions. Under this scenario alone, 19 features may be at risk of
losing more than half of their known occurrences in the LHSA region.
The mining scenario highlighted potential future conflicts between mining industry and
biodiversity protection. These are mostly located in the undeveloped parts of Cessnock and
Lake Macquarie. Unlike the other scenarios, the mining titles have relatively high overlap
with currently protected areas (10%). They also overlap with 21% of the high priority
conservation areas.
Recommendations
The results of this assessment highlight potential conflicts between development and
biodiversity protection. These conflict areas should be examined in more detail to verify the
existence of high conservation values, and to determine whether development activities in
these areas should and can be avoided to prevent substantial biodiversity losses.
There is significant conservation potential outside the existing protected areas with high
security. For example, approximately 58,800 ha of the high priority conservation areas
currently have sub-optimal protection status, providing a potential opportunity to expand
the current protected area network and increase the protection of biodiversity values within
the region.
Many of the highest priority conservation areas occur in small fragments of vegetation. This
arises because these small fragments often contain records of species or unique habitats
that cannot be found anywhere else in the region. These habitats can make a long and
lasting contribution to regional conservation objectives and have, in many instances,
maintained threatened species and communities for long periods (since clearing and
European settlement). These areas should not be discounted as low value conservation sites
based on their size and isolation.
Next steps should include verification of biodiversity values within high conservation priority
areas (potentially including ground truthing), particularly in those areas that conflict with
current development plans, verification of areas considered by stakeholders and scientists to
be of high conservation value that were not indicated as such by our analysis, and
implementation of PVAs for selected species of interest to assess impacts on extinction risk
and to evaluate mitigation or offsetting proposals.
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Table A. Predicted impact of each development scenario on the current distributions of features in the LHSA region, assuming that all native vegetation within the
development sites are cleared. The first two columns show the mean and maximum losses of feature distributions within the LHSA region under each development
scenario based on the modelled data or occurrence records used for each feature. The values in brackets give the mean impact to MNES species alone. The table also
highlights the number of features that may be at risk of losing between 50-100% of their known LHSA distribution under each scenario, as well as the proportion of high
priority conservation areas (Box A) and currently protected areas likely to be cleared. Feature-specific estimates of distribution loss can be found in Appendix 7.
ScenarioLoss of LHSA
distributions (%)Number of
features lost
Number of features losing at least
Priority conservation areas cleared (%) Current protected areas cleared (%)
Mean Max 90% 75% 50% Top 5% Top 10% Top 30% Level 1 Level 2 Level 3 Total1. Urban development
Zoned 8.2 [8.4] 100 12 0 1 6 10.1 8.9 6.9 0.8 3.1 7.2 1.3Zoned + Current strategies 9.8 [9.4] 100 16 0 2 6 10.7 10.0 8.2 0.8 3.8 10.6 1.5Zoned+ Current strategies + Other investigation areas 12.7 [11.7] 100 18 0 2 8 12.9 12.3 11.0 1.0 4.7 12.0 1.8
2. Hunter Expressway 0.18 [0.18] 1.5 0 0 0 0 0.3 0.3 0.2 0.0 0.0 0.0 0.03. Important Agricultural Lands 5.6 [5.2] 100 2 0 1 3 8.4 7.6 6.7 0.0 1.6 7.4 0.44. Cumulative development 17.1 [16.0] 100 19 0 3 10 18.7 18.1 16.7 1.1 6.1 19.1 2.25. Mining 24.9 [24.0] 100 22 0 1 7 16.4 17.2 21.2 7.5 14.5 76.4 10.3
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Box B. The conservation value of areas within the LHSA region at risk of being developed under the Cumulative Development scenario. The high priority conservation
areas (coloured red to cyan) represent sites of high conservation value based on the best 30% of the LHSA region for the 721 biodiversity features included in this
assessment. 1) Branxton-Rothbury (including Huntlee), 2) Kurri Kurri, 3) Rutherforth-Largs, 4) Tomago-Heatherbrae and 5) Southern Lake Macquarie. Development in
these locations should be avoided if possible to avoid significant biodiversity losses. Areas in dark grey (Rest) have lower conservation value and represent areas
where development is likely to have a lower overall impact on biodiversity features included in this assessment, although individual species may still be affected.
Light grey represents areas within the development footprint that are already cleared of native vegetation.
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Glossary of Terms
Algorithm A mathematical process that systematically solves a problem using well-defined rules or processes.
Area under the receiver operator curve (AUC)
A measure of how well species distribution models predict a species’ known occurrences. Values greater than 0.7 are considered to be informative (Swets 1988).
Biodiversity features Species, threatened ecological communities and other key elements selected for inclusion in spatial conservation planning.
Complementarity The degree to which a set of sites complement each other, typically in terms of their species composition, in order to represent the full scale of regional biodiversity.
Ecological community Naturally-occurring biological assemblage that occurs in a particular type of habitat.
Endangered ecological communities
Ecological communities listed under the NSW Threatened Species Conservation Act 1995 (TSC Act) or Commonwealth Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) as critically endangered, endangered or vulnerable, depending on their risk of extinction, distribution patterns and/or significant conservation value.
Grid cell One rectangular cell within a raster grid data layer.
Heuristic algorithm General class of sub-optimal algorithms that are commonly used for complex problem solving that are too large to solve by exact mathematical methods. These algorithms use rules-of-thumbs and time-saving shortcuts which do not guarantee to find the single most optimal solution but typically perform very near to it.
High priority conservation areas Set of areas that together as network best represent regional biodiversity of the target region. Each individual site contains high biodiversity values in terms of relative representation of a given biodiversity feature (such as last occurrences of very rare species or the core habitats of species). In this assessment high priority conservation areas were defined as a set of sites that together represent the best 30% of the biodiversity values within the Lower Hunter Strategic Assessment region.
Irreplaceability A measure of how easily a site can be replaced if lost.
Normalised kernel density layer A smooth density surface created by assigning a larger value to spatial features of interest, dropping away more quickly from those points.
Population Viability Analysis (PVA)
A forecast of a population’s health and extinction risk based on the species’ characteristics and environmental variability. PVA is usually unique to each species population.
Raster (data) layer Rasters are uniform grids of rectangular shape and commonly used in GIS based analyses. They typically describe characters of an area or distribution of features, each grid cell having one value within one raster layer.
Threatened species Species listed under the NSW Threatened Species Conservation Act 1995 (TSC Act) or Commonwealth Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) as critically endangered,
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endangered or vulnerable based on their risk of extinction, distribution patterns and/or their significant conservation value.
Description of threat categories
List of Commonwealth and state threat categories and their respective abbreviations used in this
report. Migratory Bird and Priority Assessment status are only used under Commonwealth
legislation. Protected status is only used under New South Wales legislation.
CE Critically endangered
E Endangered
V Vulnerable
P Protected
C China-Australia Migratory Bird Agreement
J Japan-Australia Migratory Bird Agreement
K Republic of Korea-Australia Migratory Bird Agreement
FPAL Finalised Priority Assessment List
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Frequently Used Abbreviations
ALA Atlas of Living Australia
CAMBA China–Australia Migratory Bird Agreement
DPE NSW Department of Planning and Environment
DoE Department of the Environment
EEC Endangered Ecological Community
EPBC Act Environment Protection and Biodiversity Conservation Act 1999
GIS Geographic Information System
GHVMv4 Greater Hunter Vegetation Mapping (version 4)
HCCREMS Hunter & Central Coast Regional Environmental Management Strategy
IBRA Interim Biogeographic Regionalisation of Australia
IUCN International Union for Conservation of Nature
JAMBA Japan–Australia Migratory Bird Agreement
LEP Local Environment Plan
LGA Local Government Area
LHSA Lower Hunter Strategic Assessment
MNES Matters of National Environmental Significance
NERP National Environmental Research Program
OEH NSW Office of Environment and Heritage
ROKAMBA Republic of Korea–Australia Migratory Bird Agreement
SEPP State Environmental Planning Policy
TSC Act NSW Threatened Species Conservation Act 1995
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Chapter 1 Introduction
1.1. Purpose of the research project
The National Environmental Research Program (NERP) Environmental Decisions Hub has
undertaken a study to identify conservation priorities in the Lower Hunter Valley region and
to help understand the potential impacts of potential future development on the regions’
biodiversity. This independent research will inform the regional sustainability planning and
strategic assessment process for the Lower Hunter (hereafter referred to as the Lower
Hunter Strategic Assessment or LHSA), jointly undertaken by the Australian Government and
the Government of New South Wales. This report describes the data and methods used in
the work, and the key findings. The purpose of this report is to highlight how spatial
conservation planning tools can be used to identify priority areas for conservation, and to
estimate expected biodiversity impacts arising from development, and how these can
provide highly necessary information for input into the strategic assessment process. This
research is funded by the Australian Government through the Sustainable Regional
Development Program and National Environmental Research Program (NERP), which
supports science that informs environmental policy and decision making.
The aim of this research project was to identify the impacts and ecological trade-offs of
planned and proposed development scenarios in the LHSA region, using state-of-the-art
systematic conservation planning and spatial prioritisation tools. As part of the project we
mapped the habitat suitability and distribution of 721 biodiversity features, including
threatened flora and fauna, Matters of National Environmental Significance (MNES), and
important biodiversity habitats, such as endangered ecological communities (EECs), and
important breeding or roosting habitat for a set of selected species. We identified areas of
high conservation priority in the LHSA region, reporting how these areas are currently
represented in protected areas and how the representativeness of the current reserve
network could be most effectively improved. Finally, we assessed how the areas of high
conservation value are likely to be affected under a set of planned and proposed
development scenarios, looking at both the individual and cumulative impacts of the
scenarios. For each development scenario we report the overlap of areas of potential
development with areas of high conservation priority, and the expected losses to
biodiversity. Throughout the study we report losses to biodiversity as the proportion of
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feature’s known occurrences or suitable habitat within the LHSA region that is likely to be
lost as a result of the potential development. In this report we summarise the results by
providing both the average and maximum proportion across all included features that is
likely to be lost due to development in each scenario. We also report the status and impacts
separately for Matters of National Environmental Significance (MNES). The Appendices
provided together with this report further list the analysis results for all included 721
features individually.
Our work provides a better understanding of what drives different biodiversity
outcomes, and how ecological and economic trade-offs can be balanced to achieve required
outcomes for MNES and sustainable development in the LHSA region. The approach used in
this work ensures that the best data and science is available to inform the Lower Hunter
Regional Sustainability Planning process, which is being undertaken by the New South Wales
and Australian Governments. The results will also assist the development of other strategic
approaches to environmental impact assessment under the Environment Protection and
Biodiversity Conservation Act 1999 (EPBC Act), as well as improve scenario-modelling tools
to support future strategic assessments and regional sustainability plans.
1.2. Outline of the report
This report is divided into six chapters that describe the major sections of the project. Chapter 1
describes the study area and outlines the analysis framework and the basic steps of the assessment.
It also provides a brief background to systematic conservation planning, to regional sustainability
planning including the strategic assessment process and other biodiversity assessments previously
conducted in the Lower Hunter region. Chapter 2 explains the origins of the input data for species,
EECs and other biodiversity features used in the analyses, while Chapter 3 outlines the species
distribution modelling methods used to predict the distribution of biodiversity features across the
LHSA region. Chapter 4 details the spatial prioritisation approach and identifies high priority areas
for conservation within LHSA region. We also evaluate how well the high priority conservation areas
are currently protected and examine how the representativeness of the existing protected area
network within LHSA region could be most efficiently improved. In Chapter 5, we identify the high
priority conservation areas at risk of development under five development scenarios and quantify
the potential impacts to biodiversity. Chapter 6 summarises the main findings of the report and
discusses potential limitations and uncertainties that should be considered when interpreting the
results.
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1.3. Systematic conservation planning and spatial prioritisation – planning strategically
for biodiversity
The assessment presented in this report utilises the principles and tools of systematic
conservation planning and spatial prioritisation. Systematic conservation planning is a relatively new
discipline in science that has emerged in the past few decades to guide efficient allocation of scarce
resources for biodiversity protection (Soulé 1985; Noss 1990; Margules & Pressey 2000). The
discipline was developed to address the problem of reserve design, with the aim of securing as much
of biodiversity within protected areas as quickly and with as little cost as possible. Within its brief
history, the field of systematic conservation planning has produced various approaches to aid more
strategic protection of biodiversity, and has expanded into far more complex problems such as
reserve management, restoration and the socio-economic aspects of conservation.
Some of the key principles of cost-efficient and strategic conservation planning are the concepts
of comprehensiveness, complementarity and irreplaceability. Comprehensiveness (also known as
representativeness) describes the overarching goal of conservation, that is, that all components of
biodiversity should be protected and the resulting protected area network should be representative
of the full range of biodiversity. A network of protected areas that includes all biodiversity features
(such as species or habitats) is most cost-effectively built using the principle of complementarity (i.e.
by selecting sites that complement each other). By targeting complementary sites for protection we
ensure that scarce conservation resources are used to protect currently under or un-protected
biodiversity features, which improves the protection of biodiversity most efficiently. As conservation
resources are far from adequate to protect all biodiversity (see e.g. McCarthy et al. 2012), it is typical
that protection of sites needs to be prioritised. Irreplaceability is often used as one of the guiding
principles to prioritise actions across complementing sites, reflecting how important a site is for
achieving the set conservation goals. For example, if a site contains a unique occurrence of a
biodiversity feature, it is considered to be irreplaceable and has to be protected in order to achieve a
comprehensive network of protected areas. From this, it follows that sites that host rare or very
narrowly distributed features are more irreplaceable (i.e. there are very few or no sites that could
replace them if the original sites got lost due to development). Under restricted conservation
budgets these sites are more highly prioritised over sites that host more common features.
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The majority of conservation problems are by definition of a spatial nature – that is, they involve
deciding where in the landscape to act. Spatial conservation prioritisation (sensu Moilanen et al.
2009) is a subfield within conservation planning which helps to identify the locations where desired
conservation actions should be targeted once goals and targets have been defined, typically using
the general principles explained above. Complementarity-based conservation planning differs
notably from some of the more traditional approaches which use summary statistics, such as species
richness or other component indices, to rank sites for conservation actions. These so called scoring
approaches have been proven to be inefficient as they often waste conservation resources by
selecting sites that protect some features multiple times and leave others without any protection
(Kirkpatrick 1983; Pressey & Tully 1994). Using the principle of complementarity comes with the cost
that information about the biodiversity values of all candidate sites are evaluated jointly instead of
independently, which can be a computationally complex process when prioritising sites across large
landscapes and for multiple features, such as species or habitats. However, with increasing
computing capacity, complementarity-based spatial prioritisation approaches have experienced
increasing popularity, particularly with the development of readily-accessible and efficient tools such
as Marxan (Ball et al. 2009) and Zonation (Moilanen et al. 2012), the latter of which was used in this
assessment. These tools are now applied to a range of problems globally, including the identification
of priorities for protected areas (Kremen et al. 2008), management actions (Lentini et al. 2013) and
integration with land-use planning (Gordon et al. 2009).
1.4. Regional Sustainability Planning and Strategic Assessment in Hunter Valley
In 2012, the Australian and New South Wales governments entered into an agreement to
undertake regional sustainability planning and a collaborative strategic assessment of the Lower
Hunter region. Regional sustainability planning is a collaborative process involving all levels of
government working together to foster economic prosperity, liveable communities and
environmental sustainability. In the Lower Hunter, the regional sustainability planning process has
two main stages. In the first stage, the Australian and NSW governments have been working
together to identify key knowledge gaps and scientific research to inform sustainability planning for
the Lower Hunter region. This work will complement and inform the NSW Government review of the
NSW Lower Hunter Regional Strategy and Lower Hunter Regional Conservation Plan. The second
stage will be to undertake a strategic assessment of proposed urban development and related
infrastructure corridors. The purpose of strategic assessments is to deliver a comprehensive
evaluation of future development plan impacts on environmental, social and economic sustainability
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aspects that would otherwise be assessed on project by project basis. Strategic assessments under
the EPBC Act encourage a bigger picture approach to assess how biodiversity, environmental and
heritage values that are MNES can be protected, while allowing sustainable development. They also
aim to provide greater certainty on the long-term land supply for urban and industrial development,
and on the obligations required for environmental mitigation to assure the long term persistence of
MNES.
Another strategic assessment is currently being undertaken in the Upper Hunter Valley to
estimate to potential impacts of expanding mining activity in the next 20-30 years. While this is a
separate process from the Lower Hunter strategic assessment, it is worth noting that the Upper
Hunter strategic assessment region overlaps to some degree with the one of Lower Hunter (Figure
1), and future development on both of these regions might have cumulative impacts that are not
estimated in this work.
1.5. Study area
The Lower Hunter Strategic Assessment (LHSA) will be undertaken within the local government
areas of Newcastle, Maitland, Cessnock, Lake Macquarie and Port Stephens, collectively referred to
as the ‘Lower Hunter’ in this document. The Lower Hunter is located 160 km north of Sydney and
extends over 4,290 km2 along the east coast of Australia within the State of New South Wales (NSW;
Figure 1). With more than half a million residents, the Lower Hunter region is Australia’s seventh
largest urban settlement with ongoing strong economic growth due to its diverse and stable
business sector, good national and international infrastructure and recent boom in natural resources
extraction in the Upper Hunter coalfields (NSW Government 2013). As a result, the region has
undergone a rapid expansion of housing and industry with 35,000 lots re-zoned for housing since
2008 and 3% increase in number of jobs in the past decade. The population of the Lower Hunter
Valley is projected to increase to 670,000 by 2031 (NSW Government 2013), maintaining high
demand on the urban resources such as housing supply, services, and roads and transport.
The natural environment in the Lower Hunter region is characterised by a mixture of river valley
floors fringed in the south-west and north-east by the ranges of Cessnock and Maitland LGAs. The
coast contains the expansive lake system of Lake Macquarie, the mouth of the Hunter River at
Newcastle and the extensive dune systems and estuary of Port Stephens. At present, approximately
65% of Lower Hunter has native vegetation cover and the region supports one of the three largest
river valley systems in eastern NSW, including wetlands of international and national significance
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(DECCW 2009). The Lower Hunter region is home to a number of state and federally-listed
threatened species, and the outer edges of many northern and southern ecological communities
come to meet within its borders. The area has been highlighted as a significant transition zone for
many animal and plant species between the sub-tropical influences of the north and the cooler, less
fertile conditions to the south (DECCW 2009).
Vegetation clearing in Lower Hunter has been most extensive on the valley floor, particularly in
Maitland and Newcastle, and most of the remaining native vegetation is restricted to the slopes and
ranges, with some large vegetated areas still present elsewhere. The vegetation on the valley floor
outside of these core areas is highly fragmented, with the cleared areas considered to have little
significant conservation value. Therefore, we restricted our analyses to only consider areas that are
currently covered in native vegetation considered to be important for native species (~280,149 ha or
65% of the LHSA region; Figure 1). This prioritisation footprint was generated using the best available
data for native vegetation for the LHSA region based on an updated version of the Lower Hunter
vegetation mapping produced by Parsons Brinkerhoff (Cockerill et al. 2013a; updated by Mark
Cameron, OEH, June 2014). All polygons within this layer that were assigned a Keith Formation were
included in the prioritisation footprint and converted to a raster grid with 100 m grid cells.
Figure 1. Map of Greater Hunter and the Local Government Area (LGA) boundaries. The Lower Hunter Strategic
Assessment (LHSA) region encompasses the five LGAs of Cessnock, Lake Macquarie, Maitland, Newcastle and
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Port Stephens. The dark grey areas within the LHSA boundary show the distribution of extant native
vegetation, used as a prioritisation footprint to identify locations of high conservation priority. The Upper
Hunter Strategic Assessment area, showed in blue, overlaps with the LHSA region.
1.6. Framework for mapping biodiversity patterns and prioritising options under
development scenarios
This research used a two-step framework to map the distribution of biodiversity features within
the LHSA region and identify the potential impacts of development. Here we briefly describe each
step, with further details provided in subsequent chapters.
Firstly, we identified all biological features within the region that were considered to be of
conservation importance, including protected species and threatened ecological communities
(Chapter 2). Spatial data for protected species were obtained as point locations from online public
databases (Atlas of Living Australia, BioNet), while endangered ecological community data was
either extracted as points from the Greater Hunter Vegetation Mapping (Sivertsen et al. 2011) or
provided as polygons by DoE. We then produced species distribution maps using MaxEnt, (Phillips et
al. 2006; Elith et al. 2011) or boosted regression trees (Elith et al. 2008), which give the likelihood of
observing a species in each pixel, given the environmental conditions that exist there relative to the
environmental conditions in pixels where the species is known to occur (Phillips & Dudík 2008). All
SDMs were produced at the scale of the Greater Hunter, using raster grids with a grid cell resolution
of 100m. The larger spatial extent for modelling was used to increase the amount of available
records for species and communities but also to better capture the full range of their natural
environments and therefore improve the performance of the models (Hernandez et al. 2006). The
larger region also forms a logical ecological boundary within which environmental data were readily
available. For each modelled species or EEC, the modelled output was then clipped to the LHSA area
and used in subsequent analyses (Figure 2; see Chapter 3 for more details). The point locations for
species with less than 20 records within the LHSA region and the polygon data representing the
endangered ecological communities and certain other biodiversity features were also converted to
raster grids for subsequent analyses (for details on all biodiversity data used in this assessment see
Chapter 2).
In the second phase, we used the conservation prioritisation software Zonation v.4.0 (Moilanen
et al. 2005, 2012) to identify areas of high conservation priority within the LHSA region (Chapter 4).
Zonation uses information about biodiversity features, their relative occurrences and biological
needs to create a hierarchal conservation ranking of sites across any given landscape. The
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conservation priorities were then compared to a set of proposed development scenarios to assess
the potential impacts each of the scenarios would have on the region’s biodiversity and to quantify
the feature-specific losses in terms of known occurrences or potential habitat (Chapter 5). Data
describing potential future development was provided by DoE, HCCREMS, DPE and OEH.
Figure 2. Schematic diagram representing the two-step modelling process used to generate the conservation
prioritisation. A) Occurrence data for species and endangered ecological communities (EECs) were obtained
from online databases and combined with environmental data to produce species distribution models using
MaxEnt (species) or boosted regression trees (EECs). B) These models were clipped to LHSA region and
combined with additional biological features in the spatial conservation prioritisation software, Zonation, to
identify high priority areas for conservation. We identified the best 30% of the landscape (based on the spatial
prioritisation) as high priority conservation areas after discussions with HCCREMS, DoE, and other
stakeholders.
1.7. Stakeholder and expert engagement
This assessment has been undertaken in collaboration with a number of stakeholders within the
LHSA region, with University of Melbourne researchers participating in over 40 stakeholder meetings
associated with the LHSA since 2012 to discuss research methodologies and data, and to outline
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preliminary findings of the research. This engagement has helped us to make key decisions about
which biodiversity features to include in the analyses, how those features should be weighted and
which development scenarios to assess. Some of these decisions are highlighted in this report.
Ongoing engagement with stakeholders has continued to produce useful feedback throughout this
research process.
The Department of the Environment (DoE) has provided advice in setting the analytical
framework of this assessment and identifying key outputs that will provide inputs to the LHSA. DoE
also provided a number of biodiversity, vegetation, and planning layers and advice on their
interpretation.
We have been in close consultation with the Hunter & Central Coast Regional Environmental
Management Strategy (HCCREMS) team since 2012 to establish an understanding of the current
status of biodiversity protection within the LHSA region and identify ongoing and emerging threats.
They provided spatial data describing environmental and biodiversity patterns within the Greater
Hunter region, as well as planning and land tenure data, and have been instrumental in the
interpretation of these data.
Two expert retreats were organised by HCCREMS to discuss the research methodologies and
data being used to assess conservation planning initiatives within the Greater Hunter region. The key
focus for both workshops was to bring together academic experts in conservation planning from a
number of Australian research institutions and representatives of key local government stakeholders
to develop a modelling framework and ensure that the research outputs were tailored to provide
the best possible inputs for the LHSA process. The workshops generated good discussion around the
research methodology, the use of available input data and key outputs needed to better understand
potential conservation opportunities within the Greater Hunter region.
In addition, two workshops were organised by HCCREMS to discuss aspects of the flora and
fauna modelling, with participants including representatives from HCCREMS, OEH, local councils and
biodiversity experts within the region. The purpose of these workshops was to finalise the species
pool for inclusion in the analysis, assess the accuracy of input data for, and outputs from, preliminary
SDMs and assign each species a weight based on its conservation importance within the region.
These workshops also resulted in a number of participants providing additional spatial data for some
species.
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We met with representatives from the NSW Office of Environment & Heritage (OEH) and
Department of Planning & Environment (DPE) in May 2014 to present preliminary findings from this
research and discuss how these outputs might be used to inform decision making with regard to
NSW planning strategies and the LHSA. In addition, we discussed potential options for development
scenarios to assess within this research, including identifying the most likely candidates for urban
expansion plans in the LHSA region.
This research has also been closely aligned with three other NERP projects within the LHSA
region: Wildlife Corridors Planning led by Alex Lechner (NERP Landscape & Policy Hub, University of
Tasmania, Lechner & Lefroy 2014); Mapping Community Values led by Chris Raymond (NERP
Landscape & Policy Hub, University of South Australia), and Planning for Green Open Spaces led by
Chris Ives (NERP Environmental Decisions Hub, RMIT University, Ives et al. 2014). These research
projects all investigate aspects of biodiversity conservation and urban planning within the LHSA
region.
In addition, aspects of this research have been presented to a broader audience at the Lower
Hunter Regional Forum Seminar Series and several academic conferences, including the Best
Practice Ecological Rehabilitation of Mined Lands Conference (Newcastle, NSW - 2013), the
International Congress of Ecology (London, UK - 2013), EcoTas13 (Auckland, NZ - 2013), the
Ecological Society of Australia conference (Alice Springs, NT – 2014) and the World Parks Congress
(Sydney, NSW – 2014).
1.8. Previous conservation priorities in the Lower Hunter Region
Lower Hunter Valley has been the target of several nature and conservation assessments in the
past. Numeral efforts have been made to map the distribution of native vegetation (Cockerill et al.
2013a) and threatened communities (Cockerill et al. 2013b), and the habitats of individual species of
conservation interest (Eco Logical Australia 2013; Lloyd et al. 2013; Roderick et al. 2013). The
outputs of many of these projects have been included to this work either directly as representative
distribution maps of specific species and communities (see Chapter 3), or indirectly as environmental
variables used to model the distributions of species and communities (Chapter 2).
Several assessments have also been taken to evaluate and identify important ecological
corridors, both within and beyond the Lower Hunter. These include the Wildlife Corridors of State
and Regional Significance (Scotts 2003), the Great Eastern Ranges Initiative (reviewed by Mackey et
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al. 2010) and a more recent, regional connectivity and corridor analysis funded by NERP (Lechner &
Lefroy 2014). Promotion of landscape connectivity through establishment of corridors and other
approaches aims to preserve and support ecological processes, such as dispersal, gene flow and
recolonization of empty habitat patches, and therefore improve the long-term persistence of species
and communities in the landscape. Functional connectivity can only be built upon existing locations
of suitable habitat (Hodgson et al. 2009) and using species-specific information on connectivity
needs, which is rarely available for many species. The main difference between this work and some
of the previous connectivity and corridor work done in the region is, that whereas the corridor
projects essentially aim to connect key habitats of key species and communities, this work aims to
identify where in the landscape those key habitats are across multiple species and communities.
In 2009, the NSW Department of Environment, Climate Change and Water (DECC) published the
Lower Hunter Regional Conservation Plan (DECCW 2009), identifying a suite of actions to meet the
biodiversity commitments within the Lower Hunter Regional Strategy (Department of Planning
2006). These included the identification of candidate areas for the establishment of new reserves, as
well as other areas of high conservation priority which could be protected through optional
mechanisms, such as voluntary conservation initiative, offsetting and government biodiversity
investments. The assessment was based on a suite of spatial layers, including information on native
vegetation communities, old-growth forests, extensively cleared wilderness, wetlands and
landscapes, key habitats of eight fauna assemblages and wildlife corridors of state and regional
significance (Scotts 2003). These layers were compiled into a single Biodiversity Conservation Lands
layer showing the local, regional and state significance of each location, and used together with
biodiversity targets and reserve design principles agreed to by Australian states and territories and
the Australian Government (commonly referred to as the JANIS criteria, Commonwealth of Australia
1997) to select areas of conservation priority. Identified areas of high priority included the Watagan
Ranges-Port Stephens and Wallarah Peninsula Corridors and extensions/additions to Werakata,
Karuah and Worimi Nature Reserves. Some of these areas have since then been formally protected.
The outputs of any spatial conservation planning assessment are dependent on the scale of the
assessment and the input data that is used to populate the model, as well as the method by which
priority areas are identified. This work differs most significantly from the one underpinning the
Lower Hunter Regional Conservation Plan by using much larger, mostly species level input data on
high quality habitats and known occurrences and by prioritizing areas based on local representation
of biodiversity, using the principles of complementarity and irreplaceability. The Biodiversity
Conservation Lands produced as part of the Regional Conservation Plan is mainly built upon
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vegetation patterns and proposed corridors, and the reserve design principles give high weight on
feasibility of acquisition, favouring large continuous patches of habitat on public land holdings or
major habitat linkages across the region. There are no ‘correct’ spatial conservation plans but each
model is typically designed to answer a specific conservation question. As such, the Regional
Conservation Plan is more directly tailored to support ecological processes of some species and
communities in the Lower Hunter, avoiding likely conflicts between regional development and
conservation. The prioritization presented in this report gives more detailed picture of biodiversity
patterns in Lower Hunter and forms a basis for further biodiversity and conservation assessments,
including the process of bringing together multiple planning needs in the region.
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Chapter 2 Biodiversity features included in the analysis
This assessment includes data for a range of biodiversity features, including flora and fauna
species, known species’ habitats and ecological communities. This chapter describes the sources of
these data and how they were processed prior to species distribution modelling (Chapter 3) or
inclusion in the spatial prioritisation (Chapter 4).
2.1. Flora and fauna species
2.1.1. Species data: point occurrences and modelled distributions
All species listed under Commonwealth (EPBC Act 1999) or NSW legislation (Threatened Species
Conservation Act 1995, National Parks and Wildlife Service Act 1974) were identified as potential
biodiversity features to include in the analyses. Point occurrence data for all listed species within the
Greater Hunter were downloaded from the ALA and BioNet, using the University of Melbourne data
license. Additional point data for 101 species were provided by OEH and participants of the flora
workshop.
To reduce biases due to potentially outdated and/or inaccurate spatial data, we undertook a
process of filtering point occurrence records whereby species records were excluded if they were
observed prior to 1 January 1990 to reduce uncertainties associated with spatial accuracy and
subsequent changes in environmental data, particularly vegetation cover. We also excluded those
records that had a spatial accuracy of greater than 100 m. This accuracy filtering excluded all records
with denatured location coordinates of species classified as Category 2 Sensitive Species in the OEH’s
BioNet database. OEH provided spatially accurate records for some of these species to be included
to the analyses. After the spatial accuracy filtering, the remaining data points for each species were
then compared to a raster grid of the Greater Hunter with a 100 m grid resolution and all duplicate
records within a given grid cell removed. Thus, the final data for each species represents the
distribution of occurrence records across the Greater Hunter region since 1 January 1990 where
duplicate records have been removed.
After data filtering, we had point occurrences for 712 species of amphibians, birds, mammals,
plants and reptiles with at least one occurrence within the LHSA region. For 576 species with more
than 20 occurrence points within the Greater Hunter we produced continuous distribution maps
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showing the likelihood of observing the species in any of the 100 m grid cells (Chapter 3). For the
remaining 136 species with less than 20 occurrence points, we used the filtered occurrence records
to indicate their known locations within the LHSA region. The full list of species included in the
spatial prioritisation can be found in Appendix 3.
2.1.2. Additional species layers
Five additional data layers were provided by DoE for inclusion in the Zonation analyses. These
data layers have been produced as part of other NERP projects and were considered important for
the Lower Hunter Strategic Assessment. Prior to inclusion in Zonation, all additional layers were
converted to rasters with the same extent and resolution (100 m) as the modelled species’ data and
clipped to the prioritisation area.
Distribution maps of Swift Parrot and Regent Honeyeater
The LHSA region has been shown to contain critically important winter foraging habitats for swift
parrots (Lathamus discolour), and foraging and breeding sites for regent honeyeaters (Anthochaera
phrygia) in winter and spring. Both species are listed as Endangered under the EPBC Act and as
Critically Endangered/Endangered, respectively, under the NSW TSC Act. Habitat models for the swift
parrot and regent honeyeater were produced by BirdLife Australia across the LHSA region (Roderick
et al. 2013). These models were based on careful assessment and refinement of extant observation
records combined with additional targeted surveys that allowed the potential distribution of the two
relatively rare species to be modelled. The modelling was done using the same tools (MaxEnt) as
presented in this report and utilised 15 environmental variables that described climatic,
topographical and soil conditions across the LHSA region. Habitat suitability of the two species was
predicted across the Lower Hunter region at a 100 m resolution. Details of the data collection and
modelling are given in Roderick et al. (2013). We used these two species distribution models as the
only distribution layers for these species as they provide the most comprehensive and up-to-date
data available.
Important koala habitats for conservation
The LHSA contains important habitat for koala (Phascolarctos cinereus), listed as Vulnerable both
nationally and in NSW (EPBC Act, NSW TSC Act). We produced an SDM for koala within the LHSA
region using MaxEnt as described in Chapter 3. In addition, DoE provided a map of priority koala
habitats for conservation generated by EcoLogical Australia (Eco Logical Australia 2013). This priority
map is based on expert judgement and GIS modelling and identifies priority habitats for koala as a
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weighted score of vegetation type, soil fertility, patch size, proximity to water and roads, and the
recorded presence of koala, projected across the LHSA region at a 25m resolution. It is good to
distinguish between the differences in interpretation of the two koala maps: our SDM predicts the
relatively likelihood of occurrence for koala at a given site based on the vegetation and
environmental variables listed in Table 2. In contrast, by emphasising the importance of factors such
as patch size and distance to roads, the priority map produced by EcoLogical Australia highlights
areas of koala habitats that are still relatively intact and free from human disturbance, aspects that
may be important when selecting sites for additional protection. However, in landscapes with
intensive human land use, favouring relatively undisturbed areas increases the risk of trading-off the
last remaining remnants of highly suitable habitat. Due to the differences in the preparation and
interpretation of the two maps with respect to the importance of areas for koala protection, both
layers were included in the prioritisation.
Foraging and roosting habitat of grey-headed flying fox
The grey-headed flying fox (Pteropus poliocephalus) is listed as an MNES species under the EPBC
Act, and considered vulnerable across Australia (EPBC Act 1999) and within NSW (NSW TSC Act). The
protection and management of this highly nomadic species is challenging as individuals follow
temporal and spatial changes in their food resources across Eastern Australia and occupancy of
camp sites fluctuates notably through time, with sites being occupied by up to tens of thousands of
individuals present at any one time or temporarily abandoned. Due to the highly mobile and
complex landscape use of the species and large temporal fluctuations in observations, statistical
species distribution models (described in Chapter 3) might not comprehensively capture the
importance of sites in terms of supporting long-term population dynamics. Therefore, in addition to
our species distribution model, data layers of foraging and roosting areas of the grey-headed flying
fox were included in the spatial prioritisation analysis.
The spatial data provided by DoE represented potential foraging habitat and known roosting
camps based on analyses conducted by GeoLINK (Lloyd et al. 2013). Potential foraging habitat was
based on a ranking scheme that considered flower, nectar and fruit productivity of different
vegetation types, the density of important dietary species and the seasonality of available forage
material. This information was combined to generate a foraging habitat score for each vegetation
type within the Lower Hunter valley (Lloyd et al. 2013). Data were provided as polygons based on
Map Units within the GHVMv4 (Sivertsen et al. 2011). Roosting campsites were provided as point
location data for 20 camps across the LHSA where grey-headed flying foxes are known to occur. Each
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point was buffered by the estimated canopy availability of suitable vegetation surrounding each
camp (Lloyd et al., 2013) before being converted to a raster.
2.2. Ecological Communities
2.2.1 Nationally-listed Ecological Communities
Ecological community data for communities listed under the EPBC Act were compiled by Parsons
Brinckerhoff (Cockerhill et al 2013) and provided as polygons by DoE (Table 1). All nationally-listed
ecological community layers were converted to individual raster grids for inclusion in the spatial
prioritisation. The Hunter Valley Remnant Woodlands and Open Forest data includes both areas that
have been specified in nomination and areas that have not been specified but which are consistent
with the community definition. In this case grid cells that included specified areas were given higher
value (1.0) in comparison to the unspecified ones (0.5) to reflect their higher importance within the
region.
Table 1. Nationally-listed ecological communities included in the spatial prioritisation.
Community CommonwealthStatus
Listed under the EPBC Act 1999
Littoral Rainforests and Coastal Vine thickets of Eastern Australia Critically Endangered
Lowland Rainforests of Subtropical Australia Critically Endangered
Nominated for listing under the EPBC Act 1999Subtropical and Temperate Coastal Saltmarsh VulnerableHinterland Sand Flats Forest and Woodland of the Sydney Basin Region Waiting for decision
Hunter Valley Remnant Woodlands and Open Forests Waiting for decision
2.2.2 State-listed Endangered Ecological Communities
Occurrence points for state-listed EECs within the Greater Hunter were extracted from the
survey records used as input data for constructing the GHVMv4 (Sivertsen et al. 2011) based on the
Map Unit codes. Because EECs are mutually-exclusive (i.e. two EECs cannot, by definition, occur in
the same place), we treated these occurrences records as presence-absence data where the
presence of one EEC indicated that all other EECs were absent. In addition, because these records
were undertaken as part of a systematic survey, we considered them to be spatially accurate and no
data filtering was undertaken.
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For 14 EECs with more than 20 occurrence points within the Greater Hunter we produced
continuous distribution maps showing the likelihood of observing the species in any of the 100 m
grid cells (Chapter 3). For five EECs with less than 20 occurrences we used the occurrence records to
indicate their known locations within the LHSA region. The full list of state-listed EECs included in the
analyses can be found in Appendix 3.
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Chapter 3 Modelling species potential distribution patterns
3.1 Modelling the distribution of threatened species
To identify areas important for conservation, it is necessary to understand how threatened
species and other important biodiversity features are distributed across the landscape. Distribution
data is typically available as occurrence records, where an observer has noted the location of an
organism at a given point in time. However, incomplete sampling and difficulties in detecting species
mean that these records often do not represent the entire habitat for a species. Therefore,
prioritising sites for conservation based solely on known occurrence data is likely to be biased
towards those sites that are well sampled or where common and easy to survey species are located,
with the very real risk of missing important habitats (Rondinini et al. 2006).
Species distribution modelling provides a tool that can predict the likely distribution of a species
based on known occurrence data and environmental conditions at these localities (Figure 2). The
technique is well established within the ecological literature and provides a robust and transparent
method for predicting the distribution of species from available data. Here we predict the
distribution of 576 species and 14 EECs known to occur within the LHSA , for which there were
sufficient data records to build models, using two species distribution modelling tools, MaxEnt
(species; Phillips et al. 2006) and boosted regression trees (BRTs; Elith et al. 2006). The outputs from
this modelling are then used as input data for a spatial conservation prioritisation to identify high
priority conservation areas for the LHSA region (Chapter 4). For other biodiversity features,
described in more detail in Chapter 2, we used the original point or polygon layers to represent their
distribution in the spatial conservation prioritisation of LHSA region (Chapter 4).
3.2. Species distribution modelling
We used publically available species occurrence data in a species distribution modelling
framework to characterise spatial patterns of threatened biodiversity within the LHSA region. The
collection and pre-processing of these data are described fully in Chapter 2.
3.2.3. Sampling bias layers
Species distribution modelling techniques based on presence-only data assume that the
landscape has been systematically or randomly sampled (Phillips et al. 2009) and failure to correct
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for geographic biases in the data can produce outputs that reflect the sampling effort rather than
the true species distribution (Reddy & Dávalos 2003). One option for reducing biases is to
manipulate the background data used in the modelling process by introducing a sampling bias layer
that mimics the biases in the occurrence data (Phillips et al. 2009; Kramer-Schadt et al. 2013).
The spatial distribution of species observations within the Greater Hunter region is highly biased
towards populated areas. Therefore, to reduce the influence of these observed biases in the species
occurrence data, we generated sampling bias grids for five broad taxonomic groups of amphibians,
birds, mammals, plants and reptiles. These taxon-specific bias grids were based on point data
downloaded from the ALA and BioNet for all species observed within the Greater Hunter region. For
each taxonomic group, we calculated a normalised kernel density layer from the available point data
using a 10 km radius. We chose to use all species within a taxonomic group rather than just the listed
species as it is likely that an observational technique that locates common species of a given taxa
would also locate threatened species if they were present.
Because the sampling for EECs was conducted as part of a systematic survey, no bias layer was
used in these models.
3.1.4. Environmental variables
A set of 18 ecologically-relevant environmental variables were selected as potential predictors of
the distribution of threatened species and EECs within the Greater Hunter region (Table 2). These
included variables describing the climate, vegetation, topography and soils that were available
across the entire modelling region at 100 m resolution.
Several vegetation spatial datasets were available across the Greater Hunter region, with varying
levels of accuracy. We took the best available datasets and merged these to provide a single layer
that represented the best available data at the level of Keith Formations (Keith 2004) across the
entire region. At the scale of the LHSA region, we used Keith Formation data from an updated
version of the Lower Hunter vegetation mapping produced by Parsons Brinkerhoff (Cockerill et al.
2013a; updated by Mark Cameron, OEH, June 2014), while vegetation data outside the LHSA was
obtained from the GHVMv4 spatial database (Sivertsen et al. 2011). In addition to the categorical
Keith Formation layer (final_vegetation), we also generated a layer for three Keith Formations (dry
sclerophyll forest, wet sclerophyll forest, rainforest) that represented the percentage cover of that
vegetation community within a 2km radius of each pixel in the landscape (Table 2). The percentage
cover estimates were restricted to these three Keith Formations as they have been assessed as
having a high degree of accuracy (John Hunter, Pers. Comm.)
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Table 2. Abbreviated names and definitions of mapped environmental data used as candidate predictor
variables for inclusion in species distribution models. All environmental data were available in raster format
with a resolution of 100 m.
Candidate variable Definition (Source) Unitsmean_temp Mean annual temperature (ANUCLIM) degrees Ccold_temp Mean temperature of the coldest period (ANUCLIM) degrees Chot_temp Mean temperature of the hottest period (ANUCLIM) degrees Cmean_rain Mean annual rainfall (ANUCLIM) mmseasonal_rain Mean annual temperature (ANUCLIM) mmmean_solar Mean annual solar radiation (ANUCLIM) W/m2/dayAltitude The altitude of a cell above sea level (25m DEM of Hunter Valley) metresSlope The slope of a cell (derived from Altitude) degrees
Eastness The degree to which the aspect of a cell is east (east = 1, west = -1 – derived from Altitude) index
Northness The degree to which the aspect of a cell is north (north = 1, south = -1 – derived from Altitude) index
rugg1000 Topographic ruggedness (standard deviation in altitude) in a 1000 m radius (derived from Altitude) metres
terr1000 Relative terrain position in a 1000 m radius (derived from Altitude) dimensionlessWetness Compound topographic index (derived from Altitude) dimensionless
final_vegetationKeith formation vegetation categories derived from the Parsons Brinkerhoff and Greater Hunter vegetation mapping (Sivertsen et al. 2011; Cockerill et al. 2013a)
categorical
Dry_sclerophyll_forests The percentage of cells within a 2000m radius dominated by dry sclerophyll forest (derived from final_vegetation) %
Rainforests The percentage of cells within a 2000 m radius containing rainforest (derived from final_vegetation) %
Wet_sclerophyll_forests
The percentage of cells within a 2000 m radius dominated by wet sclerophyll forest (derived from final_vegetation) %
Soils Digital Atlas of Australian Soils (CSIRO 2014) categoricalANUCLIM: Fenner School of Environment and Society, Australian National University http://fennerschool.anu.edu.au/research/products/anuclim-vrsn-61
3.2. Species distribution modelling
3.2.1. Modelling species distributions using MaxEnt
All species distribution models were constructed using MaxEnt (Phillips et al. 2006, version
3.3.3k), a freely available software. MaxEnt uses presence-only occurrence data to predict the
relative likelihood of observing a species in each pixel of the landscape, given the environmental
conditions that exist there relative to the environmental conditions in pixels where the species is
known to occur (Phillips & Dudík 2008).
Models were built for those 576 species known to occur in the LHSA region and that had a
minimum of 20 records within the Greater Hunter region, using taxa-specific sampling bias grids to
account for potential biases in the point data (Kramer-Schadt et al. 2013). For the remaining 136
species with insufficient point records to build reliable models, we used the filtered data points to
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represent their distributions within the LHSA region (for more details, see Chapter 2). All modelling
was undertaken at the scale of the Greater Hunter region, using raster grids with a grid cell
resolution of 100 m. The larger spatial extent for modelling was used to increase the amount of
available records for species and communities but also to better capture the full range of their
natural environments and therefore improve the performance of the models (Hernandez et al.
2006). The larger region forms a logical administrative boundary within which environmental data
were readily available. For each modelled feature, the predicted distribution produced at this larger
scale was then clipped to the LHSA region and used in subsequent analyses (Figure 2; see Chapter 4
for more details).
Prior to building the final model, we undertook a process of variable selection by including all 18
environmental variables in preliminary MaxEnt models for each species and then examining the
outputs to identify the most important variables across broad taxonomic groups (amphibians, birds,
mammals, plants, reptiles). Where variables were known to be spatially correlated (collinearity >
0.8), we retained the variable that had the highest mean training gain (a measure of how well a
variable describes the presence data) for all species within a taxonomic group. We then reran the
models and iteratively removed those variables that contributed little information based on their
permutation importance (less than 1% on average across all species within a taxonomic group based
on jack-knife tests) (Williams et al. 2012).
Once the parameter set was finalised, we ran the models using a five-fold cross-validation
procedure (Hastie et al. 2001). With this process, the dataset was randomly divided into five
exclusive subsets and model performance calculated by successively removing each subset, refitting
the model with the remaining data and predicting the omitted data. We assessed the mean area
under the receiver operator curve (AUC) value of each modelled species to determine the model fit,
that is, how well the models are able to predict a species’ known occurrences. We retained those
species for which the AUC value was greater than 0.7, which is generally considered to be a
threshold of an informative model (Swets 1988). For these species, we then reran the models using
the full dataset and predicted the relative likelihood of occurrence of each species across the
Greater Hunter at a spatial resolution of 100 m.
To explore the spatial uncertainties in our species’ SDMs, we used the individual predictions
from the models generated using five-fold cross-validation for each species to calculate the
coefficient of variation (CV) within each grid cell: that is, for each species we calculated the mean
and standard deviation of the predicted value in each grid cell across the five individual predictions,
and divided the standard deviation by the mean in each cell (producing the CV for each cell). When
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estimated for a single species, a CV value greater than one represents a cell where the standard
deviation across the five predictions is greater than the mean of those predictions, indicating notable
variation and therefore potentially high uncertainty in the predicted value in the particular grid cell.
To summarise the spatial patterns in SDM uncertainty, the CV values in each cell were then averaged
across all species to identify how the variation in predictive ability of the SDMs changes across the
landscape.
3.2.2 Modelling EEC distributions using boosted regression trees
We used boosted regression trees (BRT) to model the potential distributions of 14 EECs within
the LHSA region. This allowed us to utilise the absence points associated with the GHVMv4 survey
data, where the presence of a given EEC at a location necessarily indicates the absence of all other
EECs. BRT models are an advanced regression technique based on machine learning (Friedman 2002)
and are being used increasingly to model the distributions of species (Elith et al. 2006). BRT models
are capable of dealing with non-linear relationships between variables and can assess high-order
interactions, making them particularly suited for ecological data (Elith et al. 2008). BRT models are
also robust to the effects of outliers and irrelevant predictors (Leathwick et al. 2006).
We used BRT to analyse the relationship between the occurrence of each EEC and the
environment. All analyses were carried out in R (version 3.1.1) using the ‘dismo’ library (Hijmans et
al. 2013). The models were allowed to fit interactions, using a tree complexity of three and a
learning rate of 0.003. We used ten-fold cross validation to determine the optimal number of trees
for each model, giving the maximum predictive performance. BRT models have a tendency to over-
fit the training data, so the performance of the model was assessed by making predictions at sites
that were not used during model development. The probability of occurrence of each EEC was
predicted across the Greater Hunter region at a spatial resolution of 100 m. Because EECs have legal
definitions that restrict them to specific bioregions, we clipped all EEC model outputs to their listed
bioregions.
3.2.3. Model selection
The predictive power of each model was evaluated using AUC values (Hanley & McNeil 1982),
where models with an AUC value of 0.7 or greater were considered to be informative (Swets 1988).
Model outputs for species or EECs with an AUC greater than 0.7 were clipped to the LHSA region for
inclusion in the spatial prioritisation. Models for species or EECs with an AUC less than 0.7 were
either excluded from subsequent analyses or replaced with their original point data if they were
represented by less than 100 records.
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3.3. Results
3.3.1. Model performance
In general, model performance for the 590 modelled biodiversity features (576 species and 14
EECs) was high, with mean AUC values greater than 0.82 for all taxonomic groups (Figure 3; Table 3).
Seasonal rainfall, slope and local vegetation type were important drivers for all taxonomic groups. In
addition, the percentage cover of dry and wet sclerophyll forest and rainforest within a 2000 m
radius were important for all taxonomic groups. Using the iterative variable selection led to small
changes in AUC values for all species, with no consistent trend. Twenty-six species were identified as
being poorly modelled by MaxEnt based on a mean AUC value of less than 0.7 (Table 4). The majority
were common bird species, such as the Australian magpie (Cracticus tibicen) and laughing
kookaburra (Dacelo novaeguineae), with a high number of records across the Greater Hunter region.
These species typically occupy a broad range of habitat types and are, therefore, difficult to model
accurately. Those poorly modelled species with greater than 100 records (20 out of 26) were
excluded from subsequent analyses. For the remaining six species with less than 100 records (Table
4), we converted the filtered occurrence data to presence-absence rasters. Removal or conversion of
these poorly modelled species led to a final pool of 564 modelled biodiversity features (550 species
and 14 EECs) and 147 point features (142 species and 5 EECs) for inclusion in the spatial
prioritisation.
Figure 3. Boxplot of AUC values for 590
feature distributions modelled using MaxEnt
(species) or boosted regression trees (EECs)
summarised across the six broad taxonomic
groups. AUC values greater than 0.7 are
considered to be informative (Swets 1988).
The black line within the boxes shows the
median AUC values within groups. The
whiskers give the full range of values and
circles are individual features that differ
significantly from the rest of the group. The
boxes show how 50% of the values closest to
median are distributed.
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Table 3. AUC values and the relative importance of each environmental variable for 590 species distribution models summarised across taxonomic groups (mean ± standard
error), along with the number of species per group. Variables with no data for a given taxa were not included in the model for that group. Variable descriptions are given in
Table 2. Results are based on MaxEnt models for species, while EECs modelled using boosted regression trees. See Section 3.2 for further details. Outputs for individual
species can be found in Appendix 1.
Amphibians Birds Mammals Plants Reptiles EECsN 36 292 61 131 56 14AUC 0.88 (0.01) 0.84 (0.01) 0.82 (0.01) 0.90 (0.01) 0.83 (0.01) 0.93 (0.01)cold_temp 24.94 (3.39) 31.38 (1.37) 16.43 (1.94) 9.5 (1.18) 17 (2.22) -hot_temp 7.34 (1.36) 6.51 (0.51) 9.02 (1.38) 6.23 (0.89) 6.12 (1.04) -mean_rain 9.51 (1.86) 11.4 (0.91) 15.46 (2.34) - - 20.32 (3.81)seasonal_rain 15.74 (1.73) 9.99 (0.54) 14.08 (1.44) 10.15 (1.05) 10.81 (1.23) 10.65 (2.45)mean_solar - - - 23.51 (1.81) 17.86 (2.23) -slope 14.11 (1.83) 10.33 (0.57) 12.8 (0.99) 4.6 (0.6) 10.96 (1.18) 5.91 (1.17)rugg1000 3.43 (1.18) - - - - 6.72 (1.26)terr1000 - 3.33 (0.27) 3.15 (0.57) - 2.99 (0.33) -wetness - - - - - 4.04 (1.03)final_vegetation 4.76 (0.67) 3.97 (0.27) 8.46 (0.9) 6.94 (0.65) 10.06 (1.21) 9.51 (2.49)Dry_sclerophyll_forests2000 5.38 (1.21) 8.95 (0.57) 6.35 (0.79) 7.03 (0.84) 10.06 (1.36) 7.7 (1.32)Rainforests2000 3.79 (0.83) 3.52 (0.34) 3.26 (0.72) 3.35 (0.49) 2.58 (0.58) 4.82 (1.93)Wet_sclerophyll_forests2000 8.99 (2.22) 10.63 (0.64) 8.33 (1.68) 5.7 (0.69) 8.56 (1.63) 3.16 (0.85)soil - - - 19.3 (1.34) - 10.51 (1.49)
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Table 4. List of 26 poorly modelled species with mean AUC values less than 0.7. These species were either
excluded from subsequent analyses or included as point data if they had less than 100 records. Number of
records refers to observations within the Greater Hunter region.
Species Common Name NSW Status Number of records
Mean AUC
(± se)
Decision
BirdsAcanthiza pusilla Brown Thornbill P 4626 0.68 (0.01) excludeAlisterus scapularis Australian King-Parrot P 2244 0.68 (0.02) excludeAquila audax Wedge-tailed Eagle P 1206 0.65 (0.02) excludeArtamus personatus Masked Woodswallow P 34 0.66 (0.09) pointsCacatua galerita Sulphur-crested Cockatoo P 2256 0.69 (0.01) excludeColluricincla harmonica Grey Shrike-thrush P 4255 0.67 (0.01) excludeCormobates leucophaea White-throated Treecreeper P 4116 0.69 (0.01) excludeCorvus coronoides Australian Raven P 4501 0.70 (0.01) excludeCracticus tibicen Australian Magpie P 5273 0.67 (0.01) excludeDacelo novaeguineae Laughing Kookaburra P 5132 0.67 (0.01) excludeEopsaltria australis Eastern Yellow Robin P 4385 0.69 (0.01) excludeLichenostomus chrysops Yellow-faced Honeyeater P 5185 0.66 (0.01) excludeMalurus cyaneus Superb Fairy-wren P 4508 0.69 (0.01) excludeNinox connivens Barking Owl V,P,3 22 0.64 (0.12) pointsPachycephala pectoralis Golden Whistler P 4186 0.70 (0.01) excludePachycephala rufiventris Rufous Whistler P 2672 0.69 (0.01) excludePardalotus punctatus Spotted Pardalote P 4030 0.68 (0.01) excludePhilemon corniculatus Noisy Friarbird P 3678 0.67 (0.01) excludeRhipidura albiscapa Grey Fantail P 6063 0.66 (0.01) excludeStrepera graculina Pied Currawong P 4849 0.64 (0.01) excludeZosterops lateralis Silvereye P 3191 0.69 (0.01) exclude
MammalsTachyglossus aculeatus Short-beaked Echidna P 2350 0.69 (0.01) exclude
PlantsDiuris sulphurea Tiger Orchid P 40 0.69 (0.10) pointsPterostylis obtusa Blue-tongue Greenhood P 29 0.55 (0.12) points
ReptilesLialis burtonis Burton's Snake-lizard P 63 0.66 (0.08) pointsVermicella annulata Bandy-bandy P 62 0.65 (0.06) points
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3.3.2. Regional distribution patterns and uncertainty
We summed the predicted relative likelihood values in each grid cell across all species to give an
indication of the overall regional biodiversity patterns based on the species included in this analysis
(Figure 4a). In general, species were distributed unevenly across the region, with higher relative
species richness closer to the coast. Maps of the predicted distributions for each of the modelled
species are available upon request.
Figure 4. Spatial distribution patterns for species and EECs within the Greater Hunter region. A) Relative
richness of species and EECs included in the analysis, calculated by summing the outputs of the 564
distribution models retained for inclusion in the spatial prioritisation. B) Mean predictive uncertainty across
550 modelled species distributions. These data were calculated by quantifying the coefficient of variation for
each species’ MaxEnt predictions based on five-fold cross validation and then averaging across all species (see
Section 3.2.3). Grey represents areas within the Greater Hunter region that have been cleared of native
vegetation.
The mean predictive uncertainty in the model predictions across the Greater Hunter region
ranged from 0.15 – 0.54 (median: 0.26), with the highest values occurring around the Coolah Tops
and the Barrington Tops National Parks (Figure 4b). After clipping the final models to the LHSA
region, the mean spatial uncertainty across all species ranged from 0.15 – 0.39 (median: 0.21).
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3.4. Discussion
3.4.1. Overview
The modelling framework described in this report to predict the distribution of threatened
species within the LHSA region is well established amongst the conservation literature (e.g. Guisan &
Zimmermann 2000; Phillips et al. 2006; Phillips & Dudík 2008; Williams et al. 2012). It provides a
robust, transparent and repeatable method for predicting the relatively likelihood of species’
occurrences across the landscape.
3.4.2. Limitations and sources of uncertainty
When using SDMs to inform decisions, it is necessary to consider the potential limitations and
sources of uncertainty associated with the predictions (Guillera-Arroita et al. 2015). Uncertainty in
species distribution modelling can arise from two major sources: poor quality input data or
inappropriate model structures (Barry & Elith 2006). For example, inaccurate occurrence data may
result from spatial biases in sampling, imperfect detection and/or misidentification of species, while
appropriate environmental variables may not be available or mapped at a sufficiently-high resolution
to be ecologically-relevant. Both issues can lead to models that may not reflect the true distribution
of the species, with potential ramifications for the decision-making process. Also, the choice of
modelling technique may influence model predictions. Here we chose to use two well-established
methods (MaxEnt, BRT) to fully utilise the presence-only and presence-absence data available for
species and EECs, respectively, and reduce any potential biases from model structure.
The occurrence data used in this analysis were obtained from two online databases that
combine data from a range of sources, including systematic surveys, museum records and
observations by the general public. While the records were cleaned for spatial accuracy to the best
of our ability, we were unable to compensate for inaccuracies due to species misidentifications or
taxonomic changes. Therefore, it is possible that some inaccurate records were included in the
model construction.
Initial analyses identified a strong spatial bias in the occurrence data, with records concentrated
close to the coast. For this reason, we included bias layers in the modelling approach to try and
reduce the impact of this sampling bias when making predictions. Using a bias grid has previously
been shown to help address issues related to sampling bias (Kramer-Schadt et al. 2013). However,
the strong spatial bias in observation records is still evident in the species distribution models,
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showing higher levels of model uncertainty in the poorly surveyed areas of Northern and Eastern
Greater Hunter (Figure 4). These gaps in survey data may also, to some degree, affect the estimated
richness patterns in the region, although the overall pattern of increasing species richness towards
coastal areas is generally well supported.
Species distribution models are reliant on the appropriate use of ecologically-relevant
environmental data to make sensible predictions across the landscape and the availability of
accurate data at an appropriate resolution can be problematic. We were able to include a range of
environmental data describing characteristics of the climate, topography, soils and vegetation across
the Greater Hunter region. Our modelling framework used a process whereby all species within a
given taxonomic group were modelled using the same suite of environmental variables. While it
would have ideally been better to fine-tune the models for each species individually, this was not
possible due to time constraints and the large suite of species modelled. The high AUC values (Figure
3; Table 3) of most of our models indicate that, despite the somewhat generic variable selection
process across taxonomic groups instead of individual species, our approach was successful in
building distribution models that captured the known occurrences of species and notably increased
our understanding of their distribution patterns within unsurveyed areas.
It is important to understand that there may be some mismatches between species’ observed
distributions and their predicted distribution when species are absent from a site identified as having
suitable habitat. This common observation of environmentally-suitable but unoccupied sites often
results from competitive exclusion by other species, a low dispersal ability that may prevent
colonisation of otherwise suitable sites or historic factors that may have excluded species from a
previously occupied site (Guisan & Zimmermann 2000). While these areas may not currently be
occupied, they represent potentially suitable habitat for the species concerned and may still be
important for conservation purposes.
While our analysis of spatial uncertainty in the predictions indicated some areas of higher
uncertainty within the Greater Hunter region, these were located predominantly in the north-
western areas outside the LHSA region (Figure 4). All modelled distributions were clipped to the
LHSA region prior to further analysis, effectively removing these areas of higher spatial uncertainty in
the predictions. Ideally, the predictions from SDMs should be validated to ensure that they
accurately reflect the true distribution. There is a trade-off between using unvalidated SDM models
and the time required to generate potentially hundreds of highly accurate SDMs. While it is
important to recognise the potential uncertainties associated with the model predictions, SDMs
contain more information than the original point data (Rondinini et al. 2006). The models retained
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for use in the spatial prioritisation all had a high predictive value and low spatial uncertainty in the
predictions. Therefore, we feel confident that they represent the best available distribution data for
inclusion in the spatial conservation prioritisation (Chapter 4; Hermoso et al. 2015). However, we
recommend that sites highlighted by subsequent analyses as high priority for conservation or at risk
from development be surveyed as part of the decision-making process.
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Chapter 4 Spatial conservation prioritisation of the Lower
Hunter Region
4.1. Prioritising areas for conservation – the algorithm
After mapping the spatial distribution of all included biodiversity values, our next step was to
identify those areas across the LHSA region that are most important for comprehensively
representing the regional biodiversity and which provide the best available habitat for the included
features. This spatial prioritisation of sites within the LHSA region was carried out using the
conservation prioritisation software Zonation v.4.0 (Moilanen et al. 2005, 2012). Zonation is a spatial
tool that uses information about biodiversity features, their relative occurrences and biological
needs to create a hierarchal ranking of sites across any given landscape (Figure 2b). The hierarchical
ranking of the sites is created through a removal process, where the software starts by assuming
that all sites (grid cells) in the landscape are protected. It then proceeds by progressively removing
cells that cause the smallest marginal loss in conservation value (Figure 5). This is repeated until no
cells are left, the least valuable grid cells being removed first and most valuable cells being retained
until the very end. The cell removal order then produces a ranking, or priority value, for each cell.
Priority areas for conservation can then be identified simply by taking any given amount of area with
highest priority ranks, or by selecting the top fraction of ranked cells up to a given budget level.
More details about the Zonation prioritisation algorithm can be found in Appendix 2.
The conservation prioritisation produced by Zonation is primarily based on local habitat quality
and presence of biodiversity features in each grid cell. The spatial distribution of different features
drives the prioritisation in the sense that the program seeks to find a set of sites that represents the
entire regional biodiversity as comprehensively as possible (see Section 1.3). In the case of this
assessment, local habitat quality is reflected by the relative likelihood of observing the species in a
given cell, as predicted by the species-specific distribution models, assuming higher values to
indicate a better suitability and therefore quality of habitat. For features with point occurrence or
polygon data no discrimination of habitat quality between the locations can be made unless such
information is specifically provided. The program can in addition account for other ecological factors
such as connectivity of sites. However, for the analyses presented in this draft report we did not
include connectivity considerations as it was not possible to acquire the detailed information on
dispersal capability and/or connectivity needs of the considered features within the time frame. The
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top priority sites identified in this analysis therefore represent areas which are assumed to be of high
local habitat quality and where representation of the included features is maximised.
BOX 1: Creating a spatial prioritization in Zonation
Zonation can use data of any type, for example, probabilities of occurrences, known presences and absences,
numbers of individuals, populations, breeding
sites, etc. For the purpose of this illustration let
us assume that the original data values are
numbers of individuals of both features in each
grid cell, both features having a total 100
individuals within the study area. The program
starts by transforming the original values into
relative values. For example, the 5 individuals of
Feature 1 in the low-left grid cell represent 5%
(0.05) of the entire population of Feature 1
within the study region, and 15 individuals of
Feature 2 represent 15% (0.15) of Feature 2
population. The program then starts to remove
cells based on the maximum relative values
across the two features in each cell. The cell
that has the smallest maximum value will be
removed at each step, as this causes the
smallest marginal loss across both features. At
each step the relative values are updated for
the remaining cells, so that the more a feature
has lost in previous steps, the more valuable the remaining occurrences become. The program then proceeds
to remove cells until no cells are left in the landscape. The removal produces a ranking where relatively less
valuable grid cells across both features are removed first and the most valuable grid cells are retained as long
as possible. Because the program converts the original values to relative values, different data types such as
modelled distributions and point occurrences can be analysed in together. However, it is important to
understand the subtle differences in the way the different data types drive the prioritization process and how
the results can be interpreted for each data type. In practice, when the analysed spatial data is very large such
as in the case of Lower Hunter (>280,000 grid cells), the relative value of any single point occurrence is likely to
be much higher than a corresponding single value within a continuous modelled distribution surface. From this
follows that the relatively few locations of species represented with point occurrences or polygon data are
likely to have high relative values and therefore will be retained till the very end of the prioritization process.
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Figure 5. Illustration of how spatial prioritisation is created
by the Zonation software, using a simple example of two
features (Features 1 and 2) and four grid cells.
We used Zonation to build an understanding of how conservation priorities within the LHSA
region are distributed, but also to assess how they are currently protected and what the anticipated
impacts of proposed future development to these priorities are. In the first phase the prioritisation
was done without considerations of land tenure or future development plans. Hence, the
conservation value of a given grid cell was based purely on the biodiversity features within that cell,
irrespective of whether that cell is currently protected or not, or if it is proposed to be developed
under any of the development scenarios. To analyse the latter two aspects we used a built-in feature
of the Zonation software called replacement cost analysis (Cabeza & Moilanen 2006). The feature
allows artificial alteration of the cell removal order in the prioritisation process to account for the
fact that some areas might be ear-marked for development whereas others are already protected by
existing reserve networks. This process constrains Zonation to remove grid cells from certain areas
first (e.g. planned development areas) or to retain cells until the very end (e.g. existing protected
areas) regardless of their conservation value. This produces a constrained solution that can be
compared with the unconstrained solution to quantify the impact of including or excluding sites
to/from the top fraction.
We used the replacement cost feature in two ways (Table 5): first, we forced in protected areas
of the PPSA region (Figure 6) to analyse how much of the distributions of features are currently
secured under different levels of protection and what conservation potential there is left in the
currently unprotected areas (Section 4.5.3). Second, for each of the assessed development scenario
we forced out sites marked for future development and quantified the potential losses these would
cause to features in terms of lost distribution area. The assessment of development impacts is
described in more detail in Chapter 5.
Table 5. General description of the spatial analyses done using the spatial prioritization software Zonation
(version 4.0). See Chapters 4 and 5 for further details.
Spatial analysis Methodological description
Purpose Described in more detail in
Priorities for conservation
Unconstrained prioritisation
To identify areas of high conservation value across all included biodiversity features. In
Chapter 4
Unweighted
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the unweighted analysis all features are treated as equally important. In the weighted analysis features are weighted according to their Commonwealth and state threat-listing and other characteristics (see section 4.3 for details).
Weighted
Current protection Constrained prioritisation where protected areas are forced in to the top priorities
To analyse how well features are currently protected by the different types of protected areas and what conservation potential remains in the unprotected areas.
Chapter 4
Development impacts Constrained prioritisation where for each of the development scenario areas proposed for development are forced out from the top priorities
To estimate the potential losses to biodiversity if the areas proposed for development were entirely cleared.
Chapter 5
Urban
Infrastructure
Agriculture
Cumulative impact
Mining
4.2. Features used in the prioritisation
To map the spatial configuration of conservation priorities in the LHSA region we used spatial
data for 721 biodiversity features (Table 6), described in detail in Chapter 2. For each biodiversity
feature we used an individual map showing their distribution within the LHSA region as input data
for the spatial prioritisation. This included modelled distributions (described in Chapter 3) for 564
biodiversity features (550 species and 14 EECs), and point occurrence data for an additional 147
biodiversity features (142 species and 5 EECs) for which good distribution models could not be built
due to low number of observation records or poor model performance. In addition, we used polygon
data for five Commonwealth-listed EECs and five additional raster layers for regent honeyeater, swift
parrot, koala and grey-headed flying fox as described in Section 2.1.2. Of all biodiversity features
included in the prioritisation, 91 were listed as MNES (Table 6). The selection of species and other
biodiversity features for this analysis was done in collaboration with DoE and HCCREMS, together
with participants of the flora and fauna workshops. All maps of biodiversity features were clipped
with a mask to restrict the prioritisation to areas of remnant native vegetation. This prioritisation
footprint covered ~280,149 ha or 65% of the LHSA region and is described in Section 1.5.
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4.3. Weighting of features
In conservation prioritisation, species and other biodiversity features are rarely seen as equally
important for conservation (Margules & Pressey 2000). For example, there might be greater interest
in protecting a critically endangered species that is endemic to the study region in comparison to a
non-threatened species that is common and occurs across a large range. These differences in
conservation importance can be incorporated to spatial conservation planning through the use of
weights which guide the algorithms to give higher priority to areas with species of greater
importance. There are no general rules for how species and other features should be weighted
relative to each other. Weighting schemes are always case-specific and reflect the subjective values
of managers, decision-makers and the wider society. However, DoE recently produced a decision
framework for guiding the weighting of MNES in Strategic Assessments (Miller 2004, unpublished)
based on their respective national threat-category and level of endemism to the region of interest.
For the fauna and flora species, as well as for the EECs in the LHSA region, a weighting scheme
was developed in two separate workshops organised by HCCREMS in July 2013 (fauna) and May
2014 (flora and EECs). Expert panels invited by HCCREMS used information on factors such as
Commonwealth and State threat listings, and migratory behaviour, to set weights for each species or
EEC (Table 7). These weights were used in the spatial prioritisation of the LHSA region to give higher
priority to areas with occurrences and/or high habitat suitability of features with higher weights.
Final weightings for individual species and EECs are given in Appendix 3. We did not explore other
combinations of weights as part of this project but we report the result of both unweighted and
weighted solutions to illustrate how the selected weights impact the prioritisation process.
Table 6. Taxonomic breakdown of the 721 biodiversity features included in the Zonation spatial prioritisation.
MNES features are listed under the EPBC Act, while Commonwealth-listed species may be listed under the
EPBC Act or the migratory bird agreements with China, Japan or the Republic of Korea (CAMBA, JAMBA,
ROKAMBA). NSW-listed features are listed as threatened under the NSW Threatened Species Conservation Act.
Note the species and communities may be listed under multiple legislation or listed in NSW as protected but
not threatened. Therefore, the total number of features included in the prioritisation for each taxonomic
group is not necessarily the sum of the listed features for that group. The complete feature lists can be found
in Appendix 3.
Taxa Total MNES Commonwealth-
listed NSW-listed
Amphibians 40 3 3 6
Birds 316 51 51 50
Mammals 64 7 7 23
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Taxa Total MNES Commonwealth-
listed NSW-listed
Plants 212 24 24 43
Reptiles 62 0 0 2
EECs 24 6 6 17
Other habitat features 3 - - -
Total 721 91 91 141
It is worthwhile noting that as the Zonation algorithm uses values of the relative representation
of features to create the priority ranking of the landscape, higher value is inherently given to sites
that contain occurrences of narrowly distributed species. This follows the logic that any one
occurrence of a narrowly distributed species is more important than an occurrence of a common
species. Higher weights for narrowly distributed species might, therefore, have minimal additional
benefits to how well these species are represented in the top priority sites. Weights can, however,
be effectively used to distinguish between true rare endemics and species that are rare only within
the study region but common outside it. To fully explore the impact of weighting on the resulting
priorities it is essential to compare it to an otherwise identical but unweighted solution.
Table 7. Base criteria used to weight biodiversity features within the LHSA region based on their perceived
conservation importance. Weightings were derived by participants of the flora and fauna workshops. Note that
species and communities may be listed under multiple legislation. Whenever a feature had multiple threat
listings, the highest weight was used.
Fauna Flora & EECs WeightCritically endangered Commonwealth-listed 7Endangered Critically endangered 6Endangered population Endangered 5Vulnerable Vulnerable 4Migratory A Endemic 3Key Spp - important in region or might become threatened Migratory B
Nominated or possible for listing 2
Other Other 1
4.4. Protected Areas
The spatial prioritisation of the LHSA region was compared to the existing protected area
network to assess how well the conservation priorities are currently protected and what
conservation potential there remains within unprotected areas. Based on discussions with DoE and
HCCREMS, we identified all relevant protected areas to be included in the analysis and divided them
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into three categories that were assumed to represent different levels of tenure security (Table 8; see
Appendix 4 for details of the spatial layers used):
LEVEL 1 protected areas have the highest level of protection and include nature reserves, national
parks, regional parks, state conservation areas and aboriginal areas. These protected areas together
cover approximately 84,613 ha (19.7%) of the LHSA region and contain 27.9% (78,239 ha) of the
region’s remnant native vegetation (Table 8).
LEVEL 2 protected areas include flora reserves and protected areas within state forests, Ramsar
wetlands and two environments listed under State Environmental Planning Policy (coastal protection
- SEPP14 & littoral rainforest - SEPP26). In addition, they include Commonwealth-listed heritage sites
and indigenous protected areas. Note that Williamstown RAAF Airport was excluded from the Level
2 protected areas, even though it is listed as historic Commonwealth Heritage site. Salt Ash (military
target practicing area) and the indigenous Commonwealth Heritage sites around the Airport were
included based on their known biodiversity value (Eco Logical Australia 2012) and because there are
low chances of these areas being developed any time in the near future. Level 2 protected areas
cover approximately 14,635 ha (3.4%) of the LHSA region and contain 4.5 % (12,500 ha) of the
region’s remnant native vegetation (Table 8).
LEVEL 3 protected areas are regions that have a low level of formal protection for conservation
purposes but are typically thought of as having some conservation value. These include wildlife
refuges, registered property agreements and Commonwealth lands. These areas cover almost
2,570 ha (0.6%) of the LHSA region and contain 0.7% (2,023 ha) of the region’s remnant vegetation
(Table 8).
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Figure 6. Current protected areas within the LHSA region. Protected areas have been grouped into three broad
categories based on the strength of the legislative protection, with darker regions representing higher security.
Together these three levels of protected areas cover 23.7% (101,800 ha) of the LHSA region and
33.1% (92,762 ha) of the region’s remnant vegetation (Figure 6; Table 8). For each protected area
category we calculated the general overlap with the high priority conservation areas identified by
the weighted spatial prioritisation of the LHSA region (Section 4.5.1; Figure 7b). We also assessed
how well each of the protected area categories represents the regional biodiversity by calculating
the average and minimum proportion of features’ LHSA distributions that are captured by protected
areas at each level of protection.
Table 8. The protected areas within the LHSA region, showing the area (ha) and proportion (%) of the LHSA
region that is currently protected under each level of protection considered. We also show the area and
proportion of native vegetation that is protected under each level of protection.
Protected Areas LHSA region Native vegetationha % ha %
Level 1 84,613 19.7 78,239 27.9Level 2 14,635 3.4 12,500 4.5Level 3 2,570 0.6 2,023 0.7Total 101,818 23.7 92,762 33.1
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4.5. Results
4.5.1. Conservation priorities within the LHSA region
The unweighted spatial prioritisation of the LHSA region highlighted a number of highly
important areas distributed across the region (Figure 7a). Concentrations of high priority
conservation areas, which belong to the best 30% of the LHSA in terms of representing regional
biodiversity (red to cyan areas in Figure 7), in the localities of North Rothbury, Polkolbin, Abermain,
Sawyers Gully, Pelaw Main (near Kurri Kurri), Yengo National Park, Watagans National Park, Heaton
State Forest, Anna Bay, Koragang, and Tomago. (Figure 7a). Several smaller areas of high
conservation priorities are distributed in other parts of the LHSA region.
Adding species weights resulted in a slight change in the spatial arrangement of the high priority
conservation areas (Figure 7b). The most notable changes occurred across the northern boundary of
Port Stephens near Dunns Creek and Wallaroo State Forest where sites identified as high priority
conservation areas in the unweighted prioritisation were not included in the weighted prioritisation.
These changes are largely driven by the low weights given to a number of orchid species in these
50 | P a g e
Figure 7. Conservation priorities within LHSA region when all biodiversity features are a) treated equally and no
weighting is used and b) when biodiversity features are weighted differently. Note that the priority categories
are cumulative: whereas the top 5% of Lower Hunter’s conservation priorities are shown in red, the top 10%
includes both red and orange areas. The best 15% includes red, orange and yellow areas, and so on. These
priorities are based on representation of features only, with factors such as connectivity are ignored. Light grey
represents areas that have already been cleared of native vegetation.
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Figure 8. Difference in priority rankings between the unweighted and weighted solutions (Figure 7). Red
colours indicate areas where conservation priority increased after features are weighted (described in Section
4.3). Blue colours show areas which had higher priority in the unweighted solutions but where priority reduced
after incorporating weights. The intensity of the colour reflects the magnitude of the change. White areas have
identical priority in both solutions.
areas that are protected but not listed as threatened in NSW (hence given the lowest possible
weight of one – see Section 4.3 for details). These species included the orange blossom orchid
(Sarcochilus falcatus), leopard orchid (Dendrobium gracilicaule), tall wasp orchid (Chiloglottis
trilabra), Austral Lady's Tresses (Spiranthes australis), and king greenhood orchid (Pterostylis
baptistii). In contrast, there is an expansion of high priority conservation areas under the weighted
prioritisation in Cessnock and Maitland, particularly near Quorrobolong and Farley respectively. The
weighted spatial conservation prioritisation (Figure 7b) was chosen by participants in an expert
retreat held in September 2014 as the most representative of conservation priorities within the LHSA
region. Therefore, this weighting scheme has been used for all subsequent analyses and is referred
to as the weighted prioritisation hereafter. In this report, following discussions from the same
stakeholder workshop, we consider high priority conservation areas to be those cells that are in the
top 30% of the weighted spatial prioritisation.
In addition to the larger more continuous patches, several of the highest priority sites are
located in the small fragments, particularly in Maitland and Cessnock. In general, small habitat
patches tended to have higher priority ranks across the region than larger patches (Figure 9), with
this pattern largely driven by small habitat fragments that often represent the last known
occurrences of some of the features considered in this analysis. As connectivity was not accounted
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for in this analysis, the small fragments were not penalised for their small size and isolation. Half of
the high priority conservation areas of the LHSA region are within the area of Cessnock LGA, which
can be expected because of the relatively large size of the LGA (Figure 10). Interestingly, the majority
of these areas are located in the more fragmented northern areas of the LGA and, to lesser extent,
within the largely protected southern and eastern areas. On the other hand the two smallest LGAs in
the LHSA region, Maitland and Newcastle, contain more high priority conservation areas than what
could be expected based on their relative sizes. Newcastle LGA makes a particularly important
contribution to regional conservation priorities, having 1.78 times more priority conservation areas
than would be expected based on the amount of native vegetation remaining within the LGA. 53.5%
of Newcastle’s remaining native vegetation is identified within the high priority conservation areas
of the LHSA region, with these habitats largely concentrated in the coastal habitats on Kooragang
Island and around Fullerton Cove (Figure 10). Maitland LGA also contains a notable proportion of the
high priority conservation areas based on the amount of native vegetation remaining within the LGA,
with 37.3% of the remnant vegetation within this LGA identified within the high priority conservation
areas of the LHSA region. These habitats are largely distributed in small, isolated fragments across
the LGA (Figure 10). Both Maitland and Newcastle LGAs are characterised by high levels of native
vegetation clearing, with approximately 72.6% and 67.4% of their landscape cleared of native
vegetation, respectively.
Figure 9. The distribution of conservation priority ranks with respect to relative patch size within the LHSA
region. We used the proportion of native vegetation within a 2km radius of a given pixel e as a surrogate for
patch size, with smaller values representing small and more isolated patches. The horizontal line within boxes
shows the median conservation priority of grid cells within each patch size group. The whiskers give the full
range of values and open circles are individual outlier pixels that differ significantly from the rest of the group.
The boxes show how 50% of the values closest to median are distributed.
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Figure 10. Regional conservation priorities (shown in Figure 7B) within each of the LGAs in the LHSA region.
Scale bars represent distances of 5km, while light grey represents areas that have already been cleared of
native vegetation.
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Table 9. The proportion of total area and high priority conservation areas covered by LGAs in the LHSA region.
The ratios show how much of top priorities are within each LGA relative to their size (Total area) and the
relative amount of native vegetation they contain (Native vegetation). Ratio values greater than 1.0 represent
LGAs that have a higher proportion of high priority conservation areas or native vegetation than expected
based on their size, while ratio values less than 1.0 represent LGAs that have a lower proportion of high
priority conservation areas or native vegetation than expected.
LGA
Area (ha) Proportion of LHSA (%) Ratio of high priority conservation areas to
Total area
Native vegetation
Total area
Native vegetation
High priority conservation
areas
Total area
Native vegetation
Newcastle 21,497 11,021 5.0 2.5 4.5 0.89 1.78Port Stephens 97,344 75,944 22.6 19.2 22.8 1.01 1.19Cessnock 196,474 190,199 45.7 58.3 50.4 1.10 0.86Lake Macquarie 75,715 51,776 17.6 16.2 17.6 1.00 1.08
Maitland 39,239 34,357 9.1 3.8 4.8 0.52 1.24
4.5.2. Representation of features within the top priority sites
All biodiversity features considered in this analysis are represented within the high priority
conservation areas shown in Figure 11. On average, these sites cover 50.1% of the LHSA distributions
of all included biodiversity features and 67.9% of MNES feature distributions within the LHSA region.
Figure 12 shows the proportion of features’ LHSA distribution captured by the different levels of the
top priority conservation areas. Prioritising sites based on their irreplaceability and complementarity
allows highly area-efficient representation of regional biodiversity. All biodiversity features included
in this assessment can be represented within just 5% of the area of the LHSA region with extant
native vegetation (shown in red in Figure 11). These sites alone cover on average 26.4% of the LHSA
distributions of all included biodiversity features and 42.1% of the MNES LHSA distributions, and
represent some of the most critical sites for biodiversity protection within the LHSA region. Due to
their generally higher weighting, MNES features are better represented in all high priority
conservation areas in comparison to non-MNES features (Figure 12). Feature-specific values for the
proportion of each features’ LHSA distribution included within the high priority conservation areas
can be found in Appendix 5.
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Figure 11. The high priority conservation areas identified within the LHSA region based on the best 30% of the weighted spatial prioritisation ( Figure 7b). The inset boxes
highlight example areas of priority sites across the LHSA region, with important threatened biodiversity features in these areas identified in Table 10. Light grey represents
areas that have already been cleared of native vegetation. Inset boxes: 1) Yengo National Park; 2) Watagans National Park and Heaton State Forest; 3) Luskintyre and
Martinvale; 4) Anna Bay and One Mile.
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Table 10. Example of some of the important threatened biodiversity features present in the boxes identified in
Figure 11. Note that this is not an exhaustive list of all features present at the identified regions and that the
distributions of features listed in this table do not necessarily align with the distributions of the priority areas
within the boxes.
Scientific Name Common Name MNES
NSW Statu
s
EPBC Statu
s
Data type
Box 1: Yengo National ParkEucalyptus fracta Broken Back Ironbark V SDMOlearia cordata TRUE V V pointsPersoonia hirsuta Hairy Geebung TRUE E1,3 E points
Box 2: Watagans National Park and Heaton State ForestAepyprymnus rufescens Rufous Bettong V SDMHoplocephalus stephensii Stephens' Banded Snake V SDMKerivoula papuensis Golden-tipped Bat V SDMLitoria littlejohni Littlejohn's Tree Frog TRUE V V pointsLowland rainforest in NSW North Coast and Sydney Basin bioregion E SDMMacropus parma Parma Wallaby V SDM
Nominated EC Lowland TRUE CE polygon
Petroica phoenicea Flame Robin V SDMPotorous tridactylus Long-nosed Potoroo TRUE V V SDMPtilinopus magnificus Wompoo Fruit-Dove V SDMSenna acclinis Rainforest Cassia E1 SDMThylogale stigmatica Red-legged Pademelon V SDMTyto tenebricosa Sooty Owl V,3 SDM
Box 3: Luskintyre and Martinvale
Eucalyptus camaldulensis Eucalyptus camaldulensis population in the Hunter catchment E2 SDM
White box yellow box Blakely’s red gum woodland E points
Box 4: Anna Bay and One MileCharadrius mongolus Lesser Sand-plover TRUE V C,J,K SDMCrinia tinnula Wallum Froglet V SDMHaematopus fuliginosus Sooty Oystercatcher V SDMHaematopus longirostris Pied Oystercatcher E1 SDMPandion cristatus Eastern Osprey V,3 SDMProstanthera densa Villous Mint-bush TRUE V V pointsSternula albifrons Little Tern TRUE E1 C,J,K SDMTyto longimembris Eastern Grass Owl V,3 SDMXenus cinereus Terek Sandpiper TRUE V C,J,K SDMCoastal saltmarsh in the NSW North Coast, Sydney basin and South East Corner bioregion E points
Freshwater wetlands on coastal floodplains of the NSW North Coast, Sydney Basin and South East Corner bioregion E SDM
Littoral rainforest in the NSW North Coast, Sydney Basin and South East Corner bioregion E SDM
Swamp sclerophyll forest on coastal floodplains of the NSW North Coast, Sydney Basin and South East Corner bioregion E SDM
Grey-Headed Flying Fox roosting habitat otherNominated EC Littoral TRUE CE polygo
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Scientific Name Common Name MNES
NSW Statu
s
EPBC Statu
s
Data type
n
Nominated EC Saltmarsh TRUE V polygon
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There is notable variation in how well biodiversity features are represented by the high priority
conservation areas (Figure 12), these differences being largely explained by features’ respective
weight in the prioritisation process (Section 4.3) and their relative rarity within the LHSA region
(Figure 13). Across the 721 biodiversity features included in the analysis, 152 have all of their LHSA
distribution contained within the high priority conservation areas, including 34 MNES features and
43 features listed as threatened (Table 11). Some 35 features have less than 25% of their current
LHSA distribution within the priority areas, including two MNES species (Figure 11; Giant Burrowing
Frog, Heleioporus australiacus; Brush-tailed Rock-wallaby, Petrogale penicillata). For each of these
species, we used a modelled species distribution as input data to map their potential suitable habitat
within the LHSA region. All features either had relatively low weights in the prioritisation due to their
mainly low to moderate threat listing (e.g., Common Maidenhair, Adiantum atroviride and Sydney
Boronia, Boronia ledifolia) or relatively large distributions across the LHSA region. A good example of
this is the spotted quail thrush (Cinclosoma punctatum), which has 22.6% of its LHSA distribution
within the high priority conservation areas but whose suitable habitat covers approximately 40% of
the LHSA region (Figure 13).
Figure 12. The proportion of MNES and non-MNES biodiversity feature distributions within the LHSA region
that are captured by the high priority conservation areas identified in the weighted spatial prioritisation (Figure
11). The black horizontal line shows the median coverage within each group. The whiskers give the full range
of values and dots are individual outlier features that differ significantly from the rest of the group. The boxes
show how 50% of the values closest to median are distributed.
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Figure 13. The proportion of each biodiversity features’ distribution that is captured within the high priority
conservation areas (the best 30% of the landscape for biodiversity; Figure 7b and Figure 11) plotted against the
relative size of its distribution within the LHSA region, where each point represents a single biodiversity
feature. Threatened biodiversity features with all of their distributions within the high priority conservation
areas are listed in Table 11. Features with narrower distributions tend to have a higher proportion of their
distribution within the high priority conservation areas. The dotted line represents the proportion of habitat
that you would expect to be included within the top 30% of the priority for a feature that is present
everywhere in the landscape. Relative distribution sizes for features represented by points and polygons were
calculated as the number of cells where a feature was present divided by the number of cells in the
prioritisation footprint. In contrast, the relative distribution of modelled species is the sum of the model
prediction in each cell divided by the number of cells in the prioritisation footprint.
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Table 11. Threatened biodiversity features which have all of their distribution captured within the high priority
conservation areas identified by the weighted spatial prioritisation.
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4.5.3. Representativeness of the existing reserve network: gaps and opportunities
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Scientific Name Common Name MNES NSW status
EPBCstatus
Arthropteris palisotii Lesser Creeping Fern E1,3Botaurus poiciloptilus Australasian Bittern TRUE E1 E
Callitris endlicheri Black Cypress Pine, Woronora Plateau population E2
Callocephalon fimbriatum Gang-gang Cockatoo V,3Chamaesyce psammogeton Sand Spurge E1Charadrius leschenaultii Greater Sand-plover TRUE V C,J,KDarwinia peduncularis VDasyornis brachypterus Eastern Bristlebird TRUE E1 EDillwynia tenuifolia Dillwynia tenuifolia, Kemps Creek E2,VEpacris purpurascens var. purpurascens V
Eucalyptus castrensis Singleton Mallee E1Eucalyptus nicholii Narrow-leaved Black Peppermint TRUE V VEucalyptus pumila Pokolbin Mallee TRUE V VGrevillea shiressii TRUE V VLeionema lamprophyllum subsp. obovatum
Leionema lamprophyllum subsp. obovatum population in the Hunter Catchment E2,2
Limicola falcinellus Broad-billed Sandpiper TRUE V C,J,KLitoria littlejohni Littlejohn's Tree Frog TRUE V VMuehlenbeckia costata Scrambling Lignum VNeoastelia spectabilis Silver Sword Lily TRUE V VNeophema pulchella Turquoise Parrot V,3Nettapus coromandelianus Cotton Pygmy-Goose E1Ninox connivens Barking Owl V,3Olearia cordata TRUE V VPersicaria elatior Tall Knotweed TRUE V VPersoonia hirsuta Hairy Geebung TRUE E1,3 EPlanigale maculata Common Planigale VProstanthera cineolifera Singleton Mint Bush TRUE V VProstanthera densa Villous Mint-bush TRUE V VPultenaea maritima Coast Headland Pea VRostratula australis Australian Painted Snipe TRUE E1 VRulingia prostrata Dwarf Kerrawang TRUE E1 ETurnix maculosus Red-backed Button-quail VVelleia perfoliata TRUE V VCoastal saltmarsh in the NSW North Coast Sydney Basin and South East Corner bioregion E
Quorrobolong scribbly gum woodland in the Sydney Basin bioregion EUrtica incisa, Adiantum aethiopicum, Dichondra repens, Doodia aspera, Adiantum formosum, Nyssanthes diffusa E
White box yellow box Blakely’s red gum woodland EWhite gum moist forest in the NSW North Coast bioregion EGrey-Headed Flying Fox roosting habitatNominated EC Hinterland TRUE FPALNominated EC Littoral TRUE CENominated EC Lowland TRUE CENominated EC Saltmarsh TRUE V
The current protected area network covers 33.1% of the remaining native vegetation within the
LHSA region. Of this, 27.9% is within Level 1 protected areas, while Level 2 and Level 3 protected
areas each cover 4.5% and 0.7% of the remaining native vegetation, respectively (Table 12). On
average 34.9% of all biodiversity features’ LHSA distributions fall inside currently protected areas,
with MNES features generally better represented in Level 1 and Level 2 protected areas than non-
MNES features (Figure 14; Table 12). Due to their significantly larger size, the Level 1 protected areas
cover the largest proportion of biodiversity features’ LHSA distributions, providing protection to
28.5% of features’ distributions on average. Level 2 and 3 protected areas cover notably smaller
areas in the LHSA region and consequently protect smaller proportion of the distribution of features
included in this assessment (Table 12), although they do play significant roles in protecting some
species in the LHSA region (e.g., individual outliers in Figure 14). There are 67 features (7 MNES) that
are currently not represented by any protected areas, and 79 features (11 MNES) that are not
protected within the legislatively most secure Level 1 protected areas (Table 12; Table 13). Feature-
specific values for the proportion of each features’ LHSA distribution currently protected in each of
the protected area categories can be found in Appendix 5.
Although almost a quarter of the LHSA region is currently protected, there is considerable scope
for improving the representativeness and effectiveness of the regional reserve network for the
biodiversity features selected for this assessment. Of the high priority conservation areas identified
in the weighted spatial prioritisation (Figure 11), approximately 29.2% falls within currently
protected areas (Figure 15). The remaining 70% of these sites are distributed across the LHSA region
with concentrations of unprotected high priority areas around Kurri Kurri, and North Rothbury in
Cessnock; Heatherbrae, Tomago and Anna Bay in Port Stephens; and, Mandalong in the Lake
Macquarie area. These areas are crucial for the preservation of biodiversity in the LHSA region and
could form a starting point for further conservation actions.
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Figure 14. The proportion of MNES and non-MNES biodiversity features’ distributions with the LHSA region
that are covered by the three levels of protected areas. The black horizontal line shows the median coverage
within each group. The whiskers give the full range of values and dots are individual outlier features that differ
significantly from the rest of the group. The boxes show how 50% of the values closest to median are
distributed.
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Figure 15. The high priority conservation areas (the best 30% of the weighted prioritisation) within the LHSA region overlaid on the current protected area network. High
priority conservation areas that are currently protected are shown in dark grey, while the areas coloured red to cyan represent high conservation priority areas (based on
the biodiversity features included in this analysis) that have no formal protection. Regions in light grey show areas of the current protected network that are not within the
high priority conservation areas identified in this analysis.
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Table 12. Representation of biodiversity features within the current protected area network in the LHSA region. The first columns show, for each protected area level, the
proportion of the native vegetation (prioritisation footprint) and the proportion of the high (top 30%) priority conservation areas identified by the weighted prioritisation
(Figure 9) that is contained within each of these areas. The mean representation gives the average proportion of features’ LHSA distributions that is protected by each
protected area level. Numbers in square brackets refer to corresponding values for MNES features only. The following four columns show the cumulative proportion of
native vegetation and high priority conservation areas protected across the levels of protected areas, the cumulative average representation of features, and the number
of features that do not have any occurrences within the protected area network or have greater than 75% of their LHSA distributions protected. For example, protected
areas in Level 1 and 2 categories together cover 32.4% of the native vegetation within LHSA region. On average they protect 34.0% of the LHSA distributions of all
considered features but there are 70 features (7 MNES) that do not have any parts of their LHSA distribution covered by these protected areas. The last column shows how
much of features’ LHSA distributions (average and minimum) could potentially be represented if a similar amount of area was protected with site selection based on the
weighted prioritisation (Figure 9). Results for individual biodiversity features are provided in Appendix 5.
Protected Area
% of native vegetation
% of high priority
conservation areas
Mean representation
Cumulative
% of native vegetation
% of high priority
conservation areas
Mean representation
Features not
protected
Features with >75% protected
Mean (min) Zonation
representation
Level1 27.9 29.2 28.5 [36.7] 27.9 29.2 28.5 [36.7] 79 [11] 39 48.3 (13.6)Level2 4.5 6.5 5.5 [10.0] 32.4 35.8 34.0 [46.7] 70 [7] 48 52.1 (16.5)Level3 0.7 1.0 0.9 [0.9] 33.1 36.8 34.9 [47.6] 67 [7] 49 52.7 (16.9)
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Table 13. Threatened biodiversity features that do not have protection under the different levels of protected
areas within LHSA region. An additional 54 non-threatened biodiversity features included in this analysis do
not have protection in at least one of the protected area levels assessed. Results for individual species can be
found in Appendix 5.
Scientific name Common name MNESThreat status No protection within
NSW EPBC Level 1
Level 2
Level 3
Anas querquedula Garganey TRUE C,J,K XArthroteris palisotii Lesser Creeping Fern E1,3 X X XBotaurus poicilotilus Australasian Bittern TRUE E1 E X X X
Callitris endlicheriBlack Cyress Pine, Woronora plateau population
E2 X X X
Callocephalon fimbriatum Gang-gang Cockatoo V,3 X X XChamaesyce psammogeton Sand Surge E1 X X XDarwinia peduncularis V X X XDasyornis brachypterus Eastern Bristlebird TRUE E1 E X X X
Dillwynia tenuifolia Dillwynia tenuifolia, Kem's Creek E2,V X X X
Epacris pururascens var. pururascens V X X X
Eucalyptus castrensis Singleton Mallee E1 X X X
Eucalyptus nicholii Narrow-leaved Black Peppermint TRUE V V X X X
Eucalyptus pumila Pokolbin Mallee TRUE V V XGrevillea shiressii TRUE V V X X XHirundo rustica Barn Swallow TRUE C,J,K XLitoria littlejohni Littlejohn's Tree Frog TRUE V V X X XNeoastelia spectabilis Silver Sword Lily TRUE V V X X XNeophema pulchella Turquoise Parrot V,3 X X XNettapus coromandelianus Cotton Pygmy-Goose E1 X X XNinox connivens Barking Owl V,3 X X XPersicaria elatior Tall Knotweed TRUE V V XProstanthera cineolifera Singleton Mint Bush TRUE V V X X XQuorrobolong scribbly gum woodland in the Sydney Basin bioregion E X X
Turnix maculosus Red-backed Button-quail V X X XUrtica incisa, Adiantum aethioicum, Dichondra repens, Doodia aspera, Adiantum formosum, Nyssanthes diffusa E X X X
4.6. Discussion
The spatial prioritisation highlighted a number of areas within the LHSA region that have high
biodiversity value, particularly in Maitland and Newcastle LGAs. Many of these areas are small
remnant patches of vegetation that constitute the last remaining occurrences of a number of
biodiversity features, and are crucial for halting the loss of biodiversity within the LHSA region.
Comparison of the high priority conservation areas identified by Zonation and the current protected
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area network revealed that approximately 29% of the identified conservation priority areas are
currently protected to some degree, leaving 70% of these sites without any protection. These
include several critical areas around North Rothbury, Abermain, Heatherbrae, throughout Maitland
LGA and along the shores of Lake Macquarie, that belong to the most critical 5% conservation
priorities in terms of representing the full range of the LHSA regions’ biodiversity. The comparison
also revealed that, although 33% of the LHSA region has some level of protection, there are
significant gaps in the representation of biodiversity features within these areas. For example, 67
biodiversity features, including seven MNES features, currently have no protection within the LHSA
region. Therefore, there is notable conservation potential outside the current protected area
network and significant improvements in the level of biodiversity protection could be achieved with
relatively small expansions of the protected areas by strategically targeting sites that host currently
underrepresented species.
In addition, our analysis highlighted that 85% of the current protected area network within the
LHSA region has strong legal mechanisms for protection (Level 1 protected areas: nature reserves,
national parks, regional parks, state conservation areas and aboriginal areas). The remaining areas
are either contained within protected areas inside State Forests or heritage sites (Level 2) or sites
with low or voluntary mechanisms for protection not necessarily assigned for conservation purposes
(Level 3: wildlife refuges, registered property agreements and Commonwealth lands). While Level 2
and 3 protected areas are quite small in size, they currently protect biodiversity features that are not
represented in the most secure protected areas. Therefore, significant conservation gains could be
made by strengthening the level of protection in these sites, particularly where they are under
threat from future development.
It is worth noting that the paradigm of conservation reserve design often favours large patches
of habitat over small fragments as they are considered to be more robust to demographic and
environmental perturbations that may influence population persistence (Tilman et al. 1994; Cabeza
& Moilanen 2001). Although the ecological theory behind the paradigm is scientifically correct, it
does not take into account that highly fragmented landscapes with relatively recent land clearance
often have high conservation value as they contain biodiversity features that have been otherwise
largely lost from the surrounding landscape. In this assessment, we identified that many of the
remnant habitat patches, particularly in Maitland, have high conservation value. The nature of the
landscape in this region also means that these fragments are likely to be at risk of future
development and should therefore be carefully evaluated when further development decision are
made. In this assessment we did not include connectivity as one of the aspects to be prioritised
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when evaluating the conservation priority of the sites, which further increases the value of the small
fragments in the prioritisation process – prioritising sites based on connectivity often tends to place
higher priority on large intact areas whereas smaller fragments drop out from the priorities.
Although consideration of landscape connectivity is unarguably important, too heavy an emphasis
on connectivity comes with the risk of missing critically important occurrences of rare species in
their last habitat fragments. It should be kept in mind that connectivity builds upon representation
(Hodgson et al. 2009) – there is nothing to connect if the last occurrences are lost.
The high priority conservation areas identified in this analysis could be considered as a starting
point for developing a successful conservation plan in the LHSA region. However, as the prioritisation
is based on representation and local habitat quality, further work beyond the analysis of this
assessment are needed. Once species occurrences in the remaining highest quality habitat are
secured, one needs to further assess which areas are needed to maintain the ecological functions
that are crucial for species long-term persistence. This requires more detailed analyses of population
dynamics to identify the areas and structures that need to be maintained in the landscape.
4.6.1. Limitations and sources of uncertainty
The areas identified as conservation priorities in this assessment are dependent on the input
data used and it is important to recognise that a different set of biodiversity features is likely to
result in a different prioritisation solution. Given the large number and taxonomic breath of the
features used in this analysis, the sites identified are likely to encompass a wide range of habitat
types and requirements. However, it should be noted that invertebrates and fungi are not
represented at all in this assessment nor did we consider the freshwater or marine environments.
Delineating an appropriate biodiversity feature pool to include in a regional conservation planning
analysis is not a trivial task. In this work we sourced and cleaned a large dataset of biodiversity
features. We decided which of the features to include, and how to weight their relative conservation
importance, using expert knowledge available during the project. Another factor affecting the
distribution of high priority conservation areas is the scale of the assessment. The identified high
priority conservation areas in this assessment are important at the scale of the LHSA region.
However, the relative importance of these sites is likely to change if the assessment was undertaken
at a larger or smaller scale.
It also follows that the outputs from the spatial prioritisation are only as good as the input data.
Poor quality data (i.e. inaccurate distribution models and inaccurate point data) or missing data may
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result in the identification of conservation priorities that do not align with the real world. In addition,
inaccuracies in the conversion of data layers from polygon to raster, such as vegetation cover or land
use type, may also affect the spatial prioritisation. For example, we used the most recent available
vegetation mapping for the LHSA region to restrict the prioritisation area to patches of native
remnant vegetation. Any spatial inaccuracies in this layer or subsequent clearing of native vegetation
may mean that our prioritisation area does not accurately reflect the areas considered to be
important on the ground.
We have used the proportion of a features’ LHSA distribution that is protected as our metric for
assessing the potential value of a scenario and we typically report the mean and minimum
proportion of distributions across all biodiversity features. However, these estimates are not entirely
equal across all input data types, with the values for species with modelled distributions calculated
as the sum of predicted likelihoods within the area of interest, while values for point and polygon
data represent the true proportion of cells within the area of interest. Whereas this does not
diminish the value or interpretation of the analysis outputs, it is important to understand the subtle
difference between the different data types.
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Chapter 5 Identification and assessment of potential
development risks
We assessed five development scenarios to explore the potential impacts of development on the
high priority conservation areas identified in Chapter 4 and individual biodiversity features within
the LHSA region. We also identified the area of vegetation within the prioritisation footprint that is
likely to be at risk of clearing and the degree to which the scenarios overlap with the current
protected area network. These data can be used to identify potential conflicts between
development and biodiversity conservation, and to assess the relative impacts of each development
scenario. Areas of conflict highlighted in this chapter should be further investigated to quantify
potential development impacts in more detail and to assess whether further avoidance and/or
mitigation measures could be used to reduce potential biodiversity losses.
5.1. Development scenarios
The development scenarios were identified through engagement and discussions with DoE,
HCCREMS, OEH and DPE. These scenarios investigate urban, rural, infrastructure and agricultural
development that is likely, or could potentially take place in the LHSA region, and is anticipated to
lead to extensive clearing of native vegetation. We also estimated the cumulative impact if all of
these development activities were to be undertaken in the LHSA region. In addition, we included a
hypothetical scenario to investigate the potential conflicts between mining and biodiversity in the
LHSA region using information on existing mining titles and applications.
We briefly describe each of the scenarios below, with additional information about the spatial
layers used to create each scenario in Appendix 6. In all scenarios we have assumed that the
development footprint will be completely cleared, leading to the loss of all extant native vegetation
and occurrences of features within the footprint areas. We have also assumed that areas within the
current protected area network that overlap with the development footprints will be cleared. We
acknowledge that, while these assumptions were necessary for technical reasons, they may in some
instances be unrealistic. For this reason, some of the results presented in this chapter are likely to be
coarse estimations of the potential impacts.
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Scenario 1: Urban development
In this scenario the potential future losses to biodiversity from urban expansion were
investigated using three footprints that loosely describe a progressive outward expansion in built-up
areas for urban, rural and infrastructure development in the LHSA region (Figure 16).
The first footprint includes areas currently zoned to allow development that, if undertaken, is
likely to clear all remnant patches of native vegetation within the zoned area. A set of land zoning
categories to be included to this footprint were identified in collaboration with HCCREMS, DoE and
DPE (Table 14). The footprint layer was created from land use zones of the consolidated Local
Environmental Plans (LEPs) for Cessnock, Lake Macquarie, Newcastle, Maitland and Port Stephens,
and three State Environmental Planning Policy (SEPP) land use zones for Port of Newcastle, Tomago
Industrial area and Huntlee. These layers were provided by DPE and their zoning information is
correct as of 15th of February 2015. After consultations with HCCREMS and Port Stephens Planning
and Building, three groups of SP2 Special Purpose and Defence areas in Salt Ash, around the
Williamtown RAAF Airport and on the eastern side of Nelson Bay Road were removed from this
footprint.
Table 14. Local Environmental Plan (LEP) codes identified to describe land use that has a high likelihood of
extensive native vegetation clearance in the targeted site.
Land zone code Type Description
B1 Business Zones Neighbourhood CentreB2 Business Zones Local CentreB3 Business Zones Commercial CoreB4 Business Zones Mixed UseB5 Business Zones Business DevelopmentB6 Business Zones Enterprise CorridorB7 Business Zones Business ParkB8 Business Zones Metropolitan CentreIN1 Industrial Zones General IndustrialIN2 Industrial Zones Light IndustrialIN3 Industrial Zones Heavy IndustrialIN4 Industrial Zones Working WaterfrontR1 Residential Zones General ResidentialR2 Residential Zones Low Density ResidentialR3 Residential Zones Medium Density ResidentialR4 Residential Zones High Density Residential
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Land zone code Type Description
SP2 Special Purpose Zones Infrastructure
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Figure 16. Map of the Urban Development scenario, showing the non-overlapping areas of the three progressive footprints used in this scenario. Zoned = Currently zoned
areas for development; Current strategies = Investigation areas within current strategy for urban expansion; Other investigation areas = Additional investigation areas that
are identified in the Lower Hunter Council’s local strategies.
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The Salt Ash Air Weapons Range is mostly in a natural state and has been identified in previous
assessments as one of the biodiversity hotspots in Port Stephens LGA (e.g. Eco Logical Australia
2012). It has been classified as Commonwealth Heritage site and was, therefore, included in the
regional Protected Areas layer (Section 4.4). The area is still used for target practices by the military
but is in minimal use (Wonona Christian, Senior Strategic Planner, Port Stephens Council, and Mary
Greenwood, HCCREMS, pers. comm.). The SP2 Special Purpose Zone around the Williamtown RAAF
Airport and SP2 Defence area on the eastern side of Nelson Bay Road were excluded as they act as
buffer areas for the airport and are unlikely to go through any large development or land clearing
actions. The sites included in this footprint cover an approximate area of 49,940 ha and are mostly
concentrated around the city of Newcastle, along A43 highway in Maitland LGA, in Kurri Kurri and
Cessnock, and around the shores of Lake Macquarie (Figure 16).
The second footprint layer included areas within the current strategies that were identified as
potential target sites for urban and economic development in the 2006 Lower Hunter Regional
Strategy (Department of Planning 2006) as well as in Council-endorsed strategies. These areas have
since been investigated further through state and local governments, and some of them have
already been re-zoned and, therefore, overlap with the areas identified in the first footprint. The
unzoned areas are currently under further investigation to establish whether they will be zoned for
development and/or if any refinements to these areas are needed. We used the ‘URA Option 1 and
2’ layer provided by OEH for this footprint. Those areas within the footprint that do not overlap with
lands currently zoned for development cover additional 5,000 ha and are located close to main roads
and existing settlements, such as Rutherford and Medowie, and in Cameron Park (Figure 16; Table
15).
The third footprint within the urban scenario includes other investigation areas that are
identified in the Lower Hunter Council’s local strategies (Figure 16). This footprint layer includes
development proposals at various stages, such as urban releases, growth areas and urban
settlement strategies, some of which overlap both with currently zoned areas and current strategies
areas. These areas are scattered across the fringes of urban and rural development in the Lower
Hunter, including a larger freight hub development area proposed between Ashtonfield and Black
Hill. The non-overlapping area of this footprint layer covers an additional 12,250 ha, which together
with the two other footprints form the urban development scenario.
The total footprint of this scenario is approximately 67,200 ha covering 16.7% of the LHSA region
(Table 15). We note that, while the areas included in this footprint are from ongoing planning
projects at various stages, it is unrealistic to assume that all areas within the urban footprint will
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eventually be developed. For example, there are interdependencies within these layers, especially
within the current strategies and other investigation areas, which may change the likelihood of some
areas being developed in the future. We reiterate that this scenario does not represent an actual
regional development plan for the LHSA region but has been specifically designed to address the
main objectives of this study.
Scenario2: Hunter Expressway
The second development scenario investigated the explicit impacts of the Hunter Expressway on
biodiversity in the LHSA region. The Hunter Expressway project aimed to improve travel times for
motorists between Newcastle and the Upper Hunter and to reduce traffic on previously problematic
key routes across the broader network, such as the New England Highway. It involved the
construction of a four lane freeway link between the M1 Pacific Motorway near Seahampton and the
New England Highway, west of Branxton, and was one of the biggest road infrastructure projects
built in the Hunter Valley. The Hunter Expressway opened to traffic on 22 March 2014. Here we
estimated the potential losses in biodiversity features’ distributions caused by the building of the
highway.
This scenario was included in the analysis primarily to maintain comparability with other NERP
research outputs (Lechner & Lefroy 2014). We note that representing linear structures in raster
format comes with unavoidable technical shortfalls and, as such, they are not ideal development
footprints to be tested using the approach presented in this report. To create a raster based
footprint for a linear structure such as roads and highways, we assumed that all grid cells
overlapping the built highway were cleared of native vegetation. Due to data conversion constraints,
this roughly equals a 100 m wide strip along the highway, which is a coarse representation of the
actual footprint (Figure 17).
Scenario 3: Important Agricultural Lands
In this scenario we explored the impacts of potential agricultural development using the
Important Agricultural Lands data (Hunter Councils 2013) that maps the most suitable areas for
different forms of agriculture in the LHSA region. For each agricultural land use type, these areas are
defined as ”land that is capable of sustained use for agricultural activity, with appropriate
management practices, and which has the potential to contribute substantially to the ongoing
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productivity and adaptability of agriculture in the region”. From this layer we identified areas of high
value for intensive agricultural activity, including Broad Acre Agriculture, Cultivated Turf and
Viticulture, which typically lead to onsite removal of vegetation and intensive land use. These sites
were assumed to represent potential expansion of intensive agriculture in the LHSA region, likely to
result in further clearance of native vegetation within the LHSA region. The areas identified in this
footprint cover approximately 44,800 ha and are mostly concentrated on the lowland areas of
Maitland, North Cessnock and West Port Stephens (Figure 17; Table 15).
Scenario 4: Cumulative impact of all urban, rural, infrastructure and agriculture development
The fourth scenario is a combination of the first three scenarios and investigates the cumulative
impacts of urban, rural, infrastructure and agriculture development within the LHSA region (Figure
17). This footprint uses the same assumptions described for scenarios 1-3. The combined area of this
footprint is approximately 106,900 ha and 26.6% of the LHSA region.
Scenario 5: Current mining titles and applications
The final scenario explored the potential conflicts between the mining industry and biodiversity
conservation by mapping overlaps between the high priority conservation areas and existing mining
titles and title applications for coal within the LHSA region. This additional scenario was included in
this assessment to also maintain levels of comparability with other NERP research outputs that have
investigated similar scenarios within the LHSA region (Lechner & Lefroy 2014). This footprint is based
on the locations of current titles and title applications obtained from the online database MinView
(NSW Resources & Energy 2014). As there is little public spatial information about future mining sites
in the LHSA region, we used the titles and title applications as an indication of interest for mining
activities within the LHSA region. The area covered by these sites is 93,600 ha, or 23.3% of the LHSA
region (Figure 17; Table 15). We note that it is highly unlikely that all the sites within this footprint
would be mined at any stage, and therefore this scenario serves the purpose of highlighting
potential conflicts between biodiversity conservation and mining activities. These conflicts should be
explored further if mining activities are to be carried out at these sites.
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Table 15. The development scenarios within the LHSA region, showing the area (ha) and proportion of the
LHSA region that will be developed under the proposed footprints. We also show the maximum area and
proportion of native vegetation that would likely be cleared if all habitats within proposed areas of each
footprint are developed.
ScenarioTotal development footprint Native vegetation at risk of being cleared
ha % of LHSA region ha % of LHSA native vegetation
1. Urban Development 67,209 16.7 23,815 8.5 Zoned 49,938 12.4 13,952 5.0 Current strategies 5,028 1.3 2,898 1.0 Other investigation areas 12,243 3.1 6,965 2.5
2. Hunter Expressway 511 0.1 326 0.13. Important Agricultural Lands 44,786 11.2 16,670 6.04. Cumulative Development 106,866 26.6 37,992 13.65. Mining titles 93,587 23.3 71,053 25.4
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Figure 17. Development scenarios assessed in this report. Light grey area shows the distribution of remnant native vegetation within Lower Hunter Region.
Dark grey areas show sites that were assumed to be fully developed in each scenario. The cumulative scenario (scenario 4) combines scenarios 1 - 3.
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5.2. Analysis setup for assessing impacts of development
We used the replacement cost feature (described in Section 4.1) of Zonation to assess the
anticipated impacts of the five different development scenarios in the LHSA region. Here the spatial
prioritisation described in earlier chapters was repeated, but this time assuming that the proposed
development areas will be entirely cleared and any biodiversity features within them will be lost.
This is achieved by setting the program to remove grid cells from the proposed development areas
first, and only after that, continue prioritising areas outside the proposed development sites. The
impact is then measured as proportion of feature distributions lost when all development areas have
been removed.
5.3. Results
In this section we describe the potential impacts of the development scenarios within the LHSA
region. For each scenario we report the following impacts:
the maximum amount of remnant vegetation that would be cleared,
the overlap of development with high conservation priority areas,
the average proportion of biodiversity features’ LHSA distribution that is at risk to be lost if
all the development areas are entirely cleared, and
the biodiversity features most affected and to what degree.
A summary of these results can be found in Table 16, with sections providing specific details about
each scenario below. We reiterate here that all impacts on features are with respect to their
distributions within the LHSA region. We do not consider the impacts of development outside of
LHSA region nor do we report the impacts with respect to a feature’s global distribution. We also
underline that the reported impacts are based on the assumption that all native vegetation within
the development sites is lost, which is likely to be an overestimation of the true impacts.
Scenario 1: Urban development
The entire urban development footprint is estimated to clear up to 8.5% (23,800 ha) of native
vegetation within the LHSA region (Table 15). Developing these sites would also clear up to 11% of
the high priority conservation areas, with significant conflict areas likely to occur near Branxton
(Huntlee/Sweetwater), Abermain and Weston (General Industrial zone in Cessnock LEP),
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Heatherbrae and around Tomago (Table 16; Figure 18). At these sites, the development footprint
overlaps significantly with areas that have been identified as top 5% priorities within the LHSA region
(Chapter 4). In addition, there are many more scattered high priority conservation areas that overlap
with the potential development areas in this scenario, such as Morriset and Catherine Hill Bay-
Swansea stretch around Lake Macquarie, and areas north-west from Bolwarra and Bolwarra Heights
in Maitland. This scenario is also likely to clear up to 1.8% of the currently protected areas in the
LHSA region: these include 1% of the Level 1 areas, and 4.7% and 12% of the less secure Levels 2 and
3 protected areas, respectively (Table 16).
On average, developing all areas within the urban footprint is estimated to lead to the loss of up
to 12.7% of the LHSA distributions of biodiversity features (Table 17). However, there is notable
variation between features. Some 18 of the analysed features may be at risk of losing all of their
known LHSA occurrences if the areas in the urban development scenario are entirely cleared,
including the state-listed endangered sand spurge (Chamaesyce psammogeton) and cotton pygmy-
goose (Nettapus coromandelianus), and the naturalised population of the MNES Grevillea shiressii,
which is endemic to NSW (Table 17). Another ten features are at risk of losing at least 50% of their
current LHSA distribution under this scenario, of which toothed helmet orchid (Corybas pruinosus)
and honey Caladenia (Caladenia testacea) have 75% and 80% of their known occurrences
overlapping with the development footprint.
The largest impact within the urban footprint arises from currently zoned areas and is further
increased as the footprint is expanded to areas within current strategies and to other investigation
areas. The potential development of all land within the zoned areas would clear up to 7,818 ha of
vegetation and could lead to the potential disappearance of 12 biodiversity features. Approximately
7% of the high priority conservation areas would be lost if these sites were entirely cleared, including
some of the most critical sites near Huntlee, Kurri Kurri, Tomago and Kooragang wetlands which
belong to the best 5% of the LHSA region in terms of their biodiversity value. Expanding the urban
footprint to areas within the current strategies is likely to increase the urban environment in the
LHSA region only by 1.3%, and clear a relatively small additional area of up to 2,898 ha of native
vegetation. However, the potential impacts of these expansions are notable, with another four
features potentially being at risk of losing their current known occurrences within the LHSA region if
these sites are entirely developed. For the Acianthella amplexicaulis orchid the proportion of known
occurrences likely to be lost increases from 50% to 100% when the current strategies are added to
the footprint. Another two orchid species, the honey Caladenia and bird’s mouth orchid, would be at
risk of losing more than 50% of their known LHSA occurrences after these expansions. Adding the
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remaining other investigation areas would lead to an additional clearing of up to 6,965 ha of native
vegetation. These potential additions may put two more features, the endangered sand spurge
(Chamaesyce psammogeton) and Australian pratincole (Stiltia isabella), at risk of losing all their
known occurrences in the LHSA region.
Scenario 2: Hunter Expressway
Based on the analysis approach used in this assessment, the building of Hunter Expressway was
estimated to clear up to 0.1% (326 ha) of the native vegetation within the LHSA region (Table 15).
Due to its small footprint size, this development scenario has by far the smallest biodiversity impact
across the analysed scenarios: the building of the highway was estimated to result in the mean loss
of up to 0.18% of features’ LHSA distributions, with no major impacts on the representation of any of
the analysed features (Table 16). However, it should be noted that, despite the potentially small
onsite impact of this scenario, the Hunter Expressway runs across two large areas identified as highly
important for biodiversity: Branxton/Huntlee and Kurri Kurri. Other impacts not included in this
assessment, such as noise pollution and dispersal barriers, are typical issues associated with linear
infrastructure and should be assessed using appropriate methods to establish a comprehensive
understanding of the impacts (e.g., Tulloch et al. 2015). The impacts of this scenario on the regional
connectivity between habitat patches have been assessed by Lechner and Lefroy (2014).
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Table 16. Predicted impact of each development scenario on the current distributions of features in the LHSA region, assuming that all native vegetation within the
development sites are cleared. The first two columns show the mean and maximum losses of feature distributions within the LHSA region under each development
scenario based on the modelled data or occurrence records used for each feature. The values in brackets give the mean impact to MNES species alone. The table also
highlights the number of features that may lose between 50-100% of their known LHSA distribution under each scenario, as well as the proportion of high priority
conservation areas and currently protected areas likely to be cleared. Feature-specific estimates of distribution loss can be found in Appendix 7.
ScenarioLoss of LHSA
distributions (%)Number of
features lost
Number of features losing at least
Priority conservation areas cleared (%) Current protected areas cleared (%)
Mean Max 90% 75% 50% Top 5% Top 10% Top 30% Level 1 Level 2 Level 3 Total1. Urban development
Zoned 8.2 [8.4] 100 12 0 1 6 10.1 8.9 6.9 0.8 3.1 7.2 1.3Zoned + Current strategies 9.8 [9.4] 100 16 0 2 6 10.7 10.0 8.2 0.8 3.8 10.6 1.5Zoned+ Current strategies + Other investigation areas 12.7 [11.7] 100 18 0 2 8 12.9 12.3 11.0 1.0 4.7 12.0 1.8
2. Hunter Expressway 0.18 [0.18] 1.5 0 0 0 0 0.3 0.3 0.2 0.0 0.0 0.0 0.03. Important Agricultural Lands 5.6 [5.2] 100 2 0 1 3 8.4 7.6 6.7 0.0 1.6 7.4 0.44. Cumulative development 17.1 [16.0] 100 19 0 3 10 18.7 18.1 16.7 1.1 6.1 19.1 2.25. Mining 24.9 [24.0] 100 22 0 1 7 16.4 17.2 21.2 7.5 14.5 76.4 10.3
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Figure 18. Overlap of each of the assessed development footprints with identified high priority conservation areas as described in Chapter 4. Areas with remaining native
vegetation that overlap with the development footprint but which are not identified as priority sites for conservation are shown in dark grey. Areas in light grey show
locations within the footprint where native vegetation has already been cleared.
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Table 17. List of most critically impacted features in the Urban Development scenario, when all footprints in
the urban scenario are considered. The data column represents the data type that was included in the
prioritisation for each feature.
Taxa Family MNES NSW status EPBC status DataFeatures at risk of losing entire LHSA distributionAcianthella amplexicaulis plants Orchidaceae P PointsGarganey (Anas querquedula) birds Anatidae TRUE P C,J,K Points
Red-winged Parrot (Aprosmictus erythropterus) birds Psittacidae P Points
Gang-gang Cockatoo(Callocephalon fimbriatum) birds Cacatuidae V,P,3 Points
Sand Spurge (Chamaesyce psammogeton) plants Euphorbiaceae E1,P Points
Small Snake Orchid (Diuris chryseopsis) plants Orchidaceae P Points
Diuris dendrobioides plants Orchidaceae P PointsGrevillea shiressii plants Proteaceae TRUE V,P V PointsNepean Conebush(Isopogon dawsonii) plants Proteaceae P Points
Australian Little Bittern(Ixobrychus dubius) birds Ardeidae P Points
Cotton Pygmy-Goose(Nettapus coromandelianus) birds Anatidae E1,P Points
Conesticks(Petrophile canescens) plants Proteaceae P Points
Petrophile sessilis plants Proteaceae P PointsPterostylis revolute plants Orchidaceae P PointsBrown-snouted Blind Snake(Ramphotyphlops wiedii) reptiles Typhlopidae P Points
Australian Pratincole(Stiltia Isabella) birds Glareolidae P Points
Tiny Sun Orchid(Thelymitra carnea) plants Orchidaceae P Points
Tall Sun Orchid(Thelymitra media var. media) plants Orchidaceae P Points
Features at risk of losing at least 75% of LHSA distributionHoney Caladenia(Caladenia testacea) plants Orchidaceae P Points
Toothed Helmet Orchid(Corybas pruinosus) plants Orchidaceae P Points
Features at risk of losing at least 50% of LHSA distributionOriental Plover(Charadrius veredus) birds Charadriidae TRUE P J,K Points
Small Tongue Orchid(Cryptostylis leptochila) plants Orchidaceae P Points
Climbing Orchid(Erythrorchis cassythoides) plants Orchidaceae P Points
White-throated Honeyeater(Melithreptus albogularis) birds Meliphagidae P Points
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Taxa Family MNES NSW status EPBC status DataYellow Wagtail(Motacilla flava) birds Motacillidae TRUE P C,J,K Points
Bird's-mouth Orchid(Orthoceras strictum) plants Orchidaceae P Points
Tall Leek Orchid(Prasophyllum elatum) plants Orchidaceae P Points
Blue-tongue Greenhood(Pterostylis obtusa) plants Orchidaceae P Points
Scenario 3: Important Agricultural Lands
The Important Agricultural Lands scenario is expected clear up to 6.0% (16,670 ha) of native
vegetation within the LHSA region (Table 15). Approximately 7% of the identified high priority
conservation areas overlap with the potential development locations of this scenario (Table 16). The
most suitable areas for agricultural development also overlap with 7.4% of the least secure Level 3
protected areas, while there is no overlap with the most secure Level 1 protected areas and only a
small overlap of 0.6% with the Level 2 protected areas. Spatially, the impacts are concentrated
around existing agricultural lands, with significant conflict areas overlapping with the top 5%
conservation priorities around North Rothbury, East Maitland, Sawyers Gully, Woodberry, and along
the Hunter River in Maitland Vale and Oakhampton (Figure 18). On average, biodiversity features are
anticipated to lose up to 5.6% of their current LHSA distributions under this scenario (Table 16). Two
threatened bird species, the cotton pygmy-goose and red-backed button-quail, are predicted to be
most heavily impacted and may be at risk of losing their entire LHSA distribution. Four additional
features may lose more than 50% of their known LHSA occurrences if all of the sites identified in this
scenario are cleared. Of these, the endangered EEC White box-yellow box-Blakely’s red gum-
woodland may lose up to 77.8% of its currently known locations (Table 18).
Table 18. List of most critically impacted features in the Important Agricultural Lands scenario. The data
column represents the data type that was included in the prioritisation for each feature.
Taxa Family MNES NSW status EPBC status DataFeatures at risk of losing entire LHSA distributionCotton Pygmy-Goose (Nettapus coromandelianus) birds Anatidae E1,P Points
Red-backed Button-quail (Turnix maculosus) birds Turnicidae V,P Points
Features at risk of losing at least 75% of LHSA distributionWhite box yellow box Blakely’s red gum woodland EEC E Points
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Taxa Family MNES NSW status EPBC status Data
Features at risk of losing at least 50% of LHSA distributionWhite-browed Treecreeper (Climacteris affinis) birds Climacteridae P Points
Ground Cuckoo-shrike (Coracina maxima) birds Campephagidae P Points
Diuris dendrobioides plants Orchidaceae P Points
Scenario 4: Cumulative impact of all urban, rural, infrastructure and agriculture development
The cumulative development scenario, combining the first three scenarios, could lead to the loss
of up to 13.6% (37,992ha) of the native vegetation within the LHSA region (Table 15). Developing all
areas within this footprint would clear up to 16.7% of high priority conservation areas, including
nearly 20% of most critical top 5% conservation priorities. The main conflict sites of the footprint are
located around Branxton-Rothbury, Kurri Kurri, Heatherbrae-Tomago and Southern Lake Macquarie
(Figure 18). Clearing these sites is also likely to result in a potential 2.2% loss of the current
protected area network in the LHSA region, with 19.1% of Level 3 protected areas overlapping with
the potential development sites, and 1.1% and 6.1% of Level 1 and 2 protected areas, respectively
(Table 16). The cumulative impact of these developments is, on average, predicted to result in up to
17.1% reduction in feature’s current LHSA distributions. Some 19 features may lose all of their know
occurrences within the LHSA region, including four state-listed species and two MNES species. An
additional 13 features may lose at least 50% of their known LHSA occurrences. Of these, the honey
Caladenia (Caladenia testacea), toothed helmet orchid (Corybas pruin) and the endangered EEC
White box-yellow box-Blakely’s red gum-woodland may be at risk of losing 75% or more of their
current distributions (Table 19).
Table 19. List of most critically impacted features in the cumulative development impact scenario, including all
potential impacts arising from urban, rural, infrastructure and agriculture development. The data column
represents the data type that was included in the prioritisation for each feature.
Taxa Family MNES NSW status EPBC status Data
Features at risk of losing entire LHSA distributionAcianthella amplexicaulis plants Orchidaceae P PointsGarganey (Anas querquedula) birds Anatidae TRUE P C,J,K Points
Red-winged Parrot (Aprosmictus erythropterus) birds Psittacidae P Points
Gang-gang Cockatoo birds Cacatuidae V,P,3 Points
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Taxa Family MNES NSW status EPBC status Data
(Callocephalon fimbriatum)Sand Spurge (Chamaesyce psammogeton) plants Euphorbiaceae E1,P Points
Small Snake Orchid (Diuris chryseopsis) plants Orchidaceae P Points
Diuris dendrobioides plants Orchidaceae P PointsGrevillea shiressii plants Proteaceae TRUE V,P V PointsNepean Conebush (Isopogon dawsonii) plants Proteaceae P Points
Australian Little Bittern (Ixobrychus dubius) birds Ardeidae P Points
Cotton Pygmy-Goose (Nettapus coromandelianus) birds Anatidae E1,P Points
Conesticks (Petrophile canescens) plants Proteaceae P Points
Petrophile sessilis plants Proteaceae P PointsPterostylis revolute plants Orchidaceae P PointsBrown-snouted Blind Snake (Ramphotyphlops wiedii) reptiles Typhlopidae P Points
Australian Pratincole (Stiltia Isabella) birds Glareolidae P Points
Tiny Sun Orchid (Thelymitra carnea) plants Orchidaceae P Points
Tall Sun Orchid (Thelymitra media var. media)
plants Orchidaceae P Points
Red-backed Button-quail (Turnix maculosus) birds Turnicidae V,P Points
Features at risk of losing at least 75% of LHSA distributionHoney Caladenia (Caladenia testacea) plants Orchidaceae P Points
Toothed Helmet Orchid (Corybas pruinosus) plants Orchidaceae P Points
White box yellow box Blakely’s red gum woodland EEC E Points
Features at risk of losing at least 50% of LHSA distributionOriental Plover (Charadrius veredus) birds Charadriidae TRUE P J,K Points
White-browed Treecreeper (Climacteris affinis) birds Climacteridae P Points
Ground Cuckoo-shrike (Coracina maxima) birds Campephagidae P Points
Small Tongue Orchid (Cryptostylis leptochila) plants Orchidaceae P Points
Climbing Orchid (Erythrorchis cassythoides) plants Orchidaceae P Points
White-throated Honeyeater(Melithreptus albogularis) birds Meliphagidae P Points
Yellow Wagtail (Motacilla flava) birds Motacillidae TRUE P C,J,K Points
Bird's-mouth Orchid (Orthoceras strictum) plants Orchidaceae P Points
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Taxa Family MNES NSW status EPBC status Data
Tall Leek Orchid (Prasophyllum elatum) plants Orchidaceae P Points
Blue-tongue Greenhood (Pterostylis obtusa) plants Orchidaceae P Points
Scenario 5: Current mining titles and applications
The hypothetical clearing of the remaining 71,053 ha of native vegetation within the current
mining titles and applications would result in an average loss of up to 24.9% of features’ current
LHSA distributions (Table 15; Table 16). While the examined footprint is the least realistic of the
assessed scenarios, it does provide some useful insights of potential future conflicts between the
interests of the mining industry and biodiversity conservation within the LHSA region. The current
and applied mining titles encompass several areas identified as high priorities for conservation
(Figure 18). These include areas near and around Martinsville, Mandalong, Morisset, Abermain,
Mount Sugarloaf and Pokolbin. The total overlap with high priority conservation areas and current
and applied mining titles in the LHSA region is 21.2%. These titles also overlap by 7.5%, 14.5% and
76.4% with Levels 1, 2 and 3 protected areas. If all the areas within these titles was cleared, up to 22
features would be at risk of losing all their known occurrences, including two MNES features, the
eastern bristlebird (Dasyornis brachypterus) and narrow-leaved black peppermint (Eucalyptus
nicholii), and the state-listed lesser creeping fern (Arthropteris palisotii), gang-gang cockatoo
(Callocephalon fimbriatum) and common planigale (Planigale maculate). Another eight features
would be at risk of losing more than 50% of their current distribution, among these the vulnerable
MNES biconvex paperbark (Melaleuca biconvex), the endangered Woronora Plateau population of
the black cypress pine (Callitris endlicheri), and two state listed EECs (Table 20).
Table 20. List of most critically impacted features in the Mining scenario, assuming that all remaining native
vegetation within current mining titles and applications is cleared. The data column represents the data type
that was included in the prioritisation for each feature.
Taxa Family MNES NSW status EPBC status DataFeatures at risk of losing entire LHSA distributionSouthern Whiteface (Aphelocephala leucopsis) birds Acanthizidae P Points
Lesser Creeping Fern (Arthropteris palisotii) plants Davalliaceae E1,P,3 Points
Brown Tree Snake (Boiga irregularis) reptiles Colubridae P Points
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Taxa Family MNES NSW status EPBC status DataMusky Caladenia (Caladenia gracilis) plants Orchidaceae P Points
Gang-gang Cockatoo (Callocephalon fimbriatum) birds Cacatuidae V,P,3 Points
Gnat Orchid (Cyrtostylis reniformis) plants Orchidaceae P Points
Eastern Bristlebird (Dasyornis brachypterus) birds Dasyornithidae TRUE E1,P E Points
Leaden Delma (Delma plebeian) reptiles Pygopodidae P Points
Purple Donkey Orchid (Diuris punctata) plants Orchidaceae P Points
Black Rock Skink (Egernia saxatilis) reptiles Scincidae P Points
Crimson Chat (Epthianura tricolor) birds Meliphagidae P Points
Narrow-leaved Black Peppermint (Eucalyptus nicholii)
plants Myrtaceae TRUE V,P V Points
Genoplesium acuminatum plants Orchidaceae P PointsGenoplesium ruppii plants Orchidaceae P PointsDiamond Dove (Geopelia cuneata) birds Columbidae P Points
Nepean Conebush (Isopogon dawsonii) plants Proteaceae P Points
Pacific Gull (Larus pacificus) birds Laridae P Points
Conesticks (Petrophile canescens) plants Proteaceae P Points
Petrophile sessilis plants Proteaceae P PointsCommon Planigale (Planigale maculate) mammal Dasyuridae V,P Points
Small Butterfly Orchid(Sarcochilus spathulatus) plants Orchidaceae P Points
Beautiful Firetail (Stagonopleura bella) birds Estrildidae P Points
Features at risk of losing at least 75% of LHSA distributionQuorrobolong scribbly gum woodland in the Sydney basin bioregion EEC E Points
Features at risk of losing at least 50% of LHSA distributionBlack Cypress Pine, Woronora Plateau population (Callitris endlicheri)
plants Cupressaceae E2 Points
Biconvex Paperbark (Melaleuca biconvex) plants Myrtaceae TRUE V,P V SDM
White-throated Honeyeater (Melithreptus albogularis) birds Meliphagidae P Points
Bird's-mouth Orchid (Orthoceras strictum) plants Orchidaceae P Points
Pterostylis chaetophora plants Orchidaceae P Points
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Taxa Family MNES NSW status EPBC status DataThelymitra peniculata plants Orchidaceae P PointsCoastal saltmarsh in the NSW north coast Sydney basin and South East corner bioregion EEC E Points
5.4. Discussion
5.4.1. Overview: Impacts of potential development on biodiversity in the LHSA region
The general biodiversity impacts of all development scenarios are given in Table 16, with the
predicted impacts for individual features provided in Appendix 7. The impact of each development
footprint varies both in the extent of habitat likely to be cleared and the potential loss of biodiversity
feature distributions. For example, while together the zoned and current strategies areas in the
urban scenario are likely to clear a similar amount of native vegetation as the important agricultural
lands footprint (up to 16,850 ha and 16,670 ha, respectively; Table 15), the potential biodiversity
impacts of the urban development are estimated to be notably larger (average loss of 9.8% of
distributions and 17 features at risk of disappearing in the combined zoned and current strategies
footprint vs. 5.6% and 3 features in the important agricultural lands footprint; Table 16). In general,
we did not find MNES to be more highly or less impacted by the potential development in any of the
scenarios (Table 16).
Assessment of the development scenarios highlighted several potential conflicts between future
urban, infrastructure and agricultural development and biodiversity protection in the LHSA region.
While the majority of the assessed scenarios showed relatively little conflict with the existing
protected area network in the LHSA region, they had notable overlaps with several of the currently
unprotected high priority areas for conservation (Figure 18; Table 16). Significant conflicts, where
potential development overlaps with areas identified as within the most important 5% of the entire
region, were found especially around Branxton-Rothbury (including Huntlee), Kurri Kurri,
Rutherforth-Largs, Tomago-Heatherbrae and the fringes of southern Lake Macquarie (Figure 19;
Appendix 8). Without further mitigation actions the development of these sites would likely lead to
the notable reduction of regional biodiversity and the local loss of features that are difficult or
impossible to find elsewhere within the LHSA region.
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Figure 19. The conservation value of areas within the LHSA region at risk of being developed under the
Cumulative Development scenario. The high priority conservation areas (coloured red to cyan) represent sites
of high conservation value based on the best 30% of the LHSA region for the 721 biodiversity features included
in this assessment. 1) Branxton-Rothbury (including Huntlee), 2) Kurri Kurri, 3) Rutherforth-Largs, 4) Tomago-
Heatherbrae and 5) Southern Lake Macquarie. Development in these locations should be avoided if possible to
avoid significant biodiversity losses. Areas in dark grey (Rest) have lower conservation value and represent
areas where development is likely to have a lower overall impact on biodiversity features included in this
assessment, although individual species may still be affected. Light grey represents areas within the
development footprint that are already cleared of native vegetation.
The hypothetical mining scenario also revealed potential future conflicts in the LHSA region
between the mining industry and protection of regional biodiversity. The identified conflict sites
differed in most parts from the other scenarios, being concentrated on the mostly undeveloped
regions of Lake Macquarie and Cessnock LGAs (Figure 18). We re-iterate that it is highly unlikely that
all the sites included in the footprint of this scenario will be developed. Nevertheless, it is
worthwhile noting that this scenario had the highest overlap with currently protected areas within
the LHSA region (Table 16), potentially threatening not only the persistence of species and
communities within the conflict sites but also some of the already achieved conservation efforts
within the LHSA region. The Level 3 protected areas, of which over 76% overlap with current and
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applied mining titles, are likely to be at particular risk of degazetting due to their relatively weak
legislative status.
With the exception of the Hunter Expressway, all development scenarios risk losing ll know
occurrences of a number of biodiversity features, assuming that all native vegetation within the
scenario footprints was cleared (Table 17; Table 18; Table 19; Table 20). A majority of these features
are bird or plant species, with orchids forming the single biggest taxonomic group within the
identified highly impacted features. We note that many of these features are very rare within the
LHSA region but, particularly in the case of non-threatened species, may be near the limit of their
geographical range and, therefore, be more common outside the assessment area. Some of the bird
species, such as gargany (Anas querquedula) and Cotton Pygmy Goose (Nettapus coromandelianus)
are vagrant visitors to New South Wales, and as such do not necessarily constitute high conservation
priority. A majority of the highly impacted species and communities are also represented by point
occurrences, and for some of them the points do not correctly reflect their true distribution within
the study region. Nevertheless, as these shortfalls are constant across the scenarios, the number of
highly impacted features provides a measure to compare the relative impacts of different
development activities, and highlights species and communities that, irrespective of their threat
status, are at risk of disappearing from the LHSA region.
Overlaying the priority ranking of the LHSA region on each of the development footprints (Figure
18; Appendix 8) provides an opportunity to identify those areas where development is likely to result
in the loss of high priority conservation areas. Development should be avoided in these sites where
possible to prevent the loss of key biodiversity features and habitats. If avoidance is not possible,
mitigation actions should be undertaken. In contrast, the weighted prioritisation map (Figure 7b) can
be used to identify areas of the landscape with a low conservation priority that may be better suited
for development activities (Bekessy et al. 2012). Combining such information with planning tools can
help to identify areas for development that minimise the impact to biodiversity while meeting
planning requirements (Pettit et al. 2015).
5.4.2. Limitations and sources of uncertainty
The exact impact values for each scenario should be interpreted with caution as they are based
on the assumption that all areas marked for development will be entirely cleared. This may not
necessarily be a realistic assumption, especially for the mining titles and areas of rural residential
development. It is nevertheless likely that these land use types will have significant local impacts to
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biodiversity. Further inaccuracies can arise from data conversions, where line and polygon shapes
are forced into raster grid formats which are necessary for this type of analysis. These can create
inaccurate overlaps between biodiversity features distributions and development scenarios, both in
terms of false overlaps and false non-overlaps. These types of errors in spatial data conversions are
typical and unfortunately non-trivial to solve. The outputs of these assessments nevertheless
provide useful insights on the relative potential conflicts between development options and
conservation that can be investigated further with more detailed analyses. We also note that we did
not assess the likely indirect impacts of development, such as dispersal barriers, noise and pollution
nearby impact sites and increased fragmentation of the landscape, which can further negatively
affect biodiversity features and the persistence of populations in the landscape. This means that
despite the potential coarseness and errors in the footprint estimates, our approach is very likely to
be a conservative underestimate of the full scale ecological impacts of the assessed development
plans.
We also reiterate that the outputs from the spatial prioritisation are only as good as the input
data. Poor quality data (i.e., inaccurate distribution models and inaccurate point data) or missing
data may result in the identification of potential biodiversity losses that does not align with the real
world. In some cases, particularly for some of the included plant species, the data filtering process
(described in Section 2.1.1) may have also removed old records of occurrences that are still extant
today. For example, biodiversity features that are under-represented in the assessment (e.g., have
fewer occurrence records than real world occurrences) may appear to be more heavily impacted by
development than might be realistic. Conversely, the impacts of development on potentially over-
represented biodiversity features (e.g., models that overpredict the extent of a feature’s
distribution) may be underestimated. While these potential biases are unavoidable in the absence of
perfect knowledge about biodiversity distributions, we provide a relative assessment of the potential
impacts of development based on the available data. The purpose of this study is to highlight and
quantify the potential conflicts between development and conservation across the entire region.
Further investigation into the identified conflict areas should be carried out to provide detailed
accurate impact assessments.
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Chapter 6 Synthesis and future directions
6.1 Summary
Using our proposed framework for regional-scale strategic impact assessments, we were able to
identify high priority areas for conservation within the LHSA region based on the distribution of 721
biodiversity features (including 91 MNES and 141 state-listed features). We identified potential
conflicts between these high priority conservation areas and potential development within the LHSA
region, quantifying the anticipated losses to biodiversity and individual biodiversity features if that
development goes ahead. In this final chapter, we synthesise the main findings and discuss some of
the limitations of the assessment. We also highlight some recommendations to guide the LHSA and
identify options for future work.
6.1.1. Conservation priorities and current protection of biodiversity in the LHSA region
The spatial prioritisation identified large areas within the LHSA region that are highly important
for the conservation for the biodiversity features included in this assessment. These high priority
conservation areas (considered here to be the best 30% of the LHSA region with native vegetation)
are distributed across the entire region, with important areas located around, North Rothbury,
Pelaw Main (near Kurri Kurri), Heaton State Forest, Watagans National Park, Kooragan Island,
Tomago, and Anna Bay (Figure 7B). In many cases, particularly in Maitland LGA, these high priority
conservation areas are small fragments of native vegetation that may contain the last known
occurrences of some the biodiversity features included in this assessment. Cessnock, the largest LGA
in the LHSA region, contains half the identified high priority conservation areas (Table 9). However,
the majority of the high priority conservation areas are located in the more fragmented northern
areas of the LGA and, to lesser extent, within the largely protected southern and eastern areas
(Figure 10). In the two smallest LGAs, Newcastle and Maitland, the majority of native vegetation has
already been cleared, but the remaining areas hold notable conservation value with approximately
50% and 40%, respectively, of the two LGA’s vegetated areas identified as high priorities for
conservation (Table 9).
All 721 biodiversity features included in this assessment are represented within the high priority
conservation areas, with, on average, half of their LHSA distributions captured within these areas
(68% of the distributions of MNES features)(Figure 12 and Figure 13). In addition, 152 biodiversity
features (34 MNES) have their entire LHSA distributions within the high priority conservation areas
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(Table 11). The assessment highlighted that, while 33% of the remnant native vegetation within the
LHSA region is currently protected, 63.2%% of the high priority conservation areas are currently
unprotected , and another 7.6% is protected by only the intermediate or low security protected
areas (Figure 15; Table 12). In contrast, several of the unprotected areas are threatened by potential
future development. While many biodiversity features are represented within the current protected
area network, there are 80 features (12 MNES) that do not occur with the most secure protected
areas and 67 features (7 MNES) that are not protected at all (Figure 14; Table 13). The currently
unprotected high priority conservation areas (Figure 15), therefore, provide notable conservation
potential and significant improvements in the level of biodiversity protection in the LHSA region
could be achieved with relatively small expansions of the protected areas strategically targeted to
sites with currently underrepresented species and communities. Special attention is required to
maintain the biodiversity values of the small vegetation fragments identified as high priority for
conservation. It is likely that conservation actions beyond land tenure changes are required,
including active management and restoration of lost habitats to secure the long-term persistence of
the rarest species and communities within these fragments.
6.1.2. Impacts of potential development on biodiversity in the LHSA region
In our assessment we investigated the potential impacts of five development scenarios (Figure
17). Of these, four looked at realised, likely and potential urban, infrastructure and agriculture
development within the LHSA region and one compared the overlap of existing and applied mining
titles and biodiversity values within the region. We re-iterate here that, although the spatial data
used in these scenarios were in some parts provided by NSW DPE, the assessed scenarios do not
necessarily represent actual plans for future development but rather were developed to help
understand where potential future conflicts between development and biodiversity conservation
may occur.
Our results highlighted several conflict areas where potential future development is likely to
overlap with the identified high priority conservation areas and where notable biodiversity losses
can be expected if the development proceeds to clear native vegetation (Figure 18). These sites are
mostly concentrated along the urban and rural fringes and along main roads in the LHSA region, with
larger areas of conflicts identified in Huntlee, North Rothbury, Pelaw Main (near Kurri Kurri),
Tomago, Heatherbrae and around the fringes of Lake Macquarie. Many of these sites belong to the
most important 5% of the LHSA region in terms of their biodiversity value and without further
mitigation actions, the development of these sites would likely lead to the loss of features that are
difficult or impossible to find elsewhere within the region. The majority of the assessed development
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scenarios pose little threat to current protected areas in the LHSA region but the overlap between
unprotected high priority areas and proposed and potential development in LHSA is notable (up to
21% depending on the scenario)(Table 16). The cumulative development scenario that combined all
urban, infrastructure and agricultural development was estimated to result in the clearance of up to
38,000 ha of native vegetation and the average loss of up to 17% of biodiversity features’ LHSA
distributions. Nearly all development scenarios were highlighted as potentially clearing the last
known occurrences of some biodiversity features within the LHSA region, with likely implications for
their persistence within the region. More detailed assessment of these conflict sites is needed to
fully understand the potential risks of biodiversity losses that would follow from developing these
sites and to assess most suitable mitigation actions.
Comparison of mining titles and high priority conservation areas also revealed potential future
conflicts between the mining industry and biodiversity conservation in the LHSA region. The current
and applied mining titles cover 23% of the LHSA region, concentrated on the mostly undeveloped
regions of Lake Macquarie and Cessnock LGAs (Figure 18). This scenario differed from the other
scenarios, not only in the spatial extent and distribution of the footprint, but also due to the
relatively high overlap with existing protected areas (Table 16). Ten percent of the existing protected
area network in the LHSA region falls within these mining titles, with the legislatively less-secure
Level 3 protected areas overlapping by 76% with the mining scenario footprint. If mining becomes
more active within these areas, not only will biodiversity features be at risk of being lost, but also the
legacy of past conservation efforts.
6.2. Discussion and future directions
In this assessment we have shown how systematic spatial prioritisation methods provide a
coherent approach to dealing with many data layers describing biodiversity features and potential
development scenarios in a way that cannot be achieved by the human brain within a deliberative
process. Consequently, the results presented in this report represent potentially extremely valuable
inputs to the deliberative process that significantly reduces the cognitive burden on those involved.
We have undertaken a transparent and repeatable analysis of the biodiversity values of the region
and the likely impacts of several development scenarios, using peer-reviewed methods with a long
history of use in conservation planning. The analysis presented in this study can be repeated for any
combination of biodiversity features and development scenarios, or if needed, conducted on
different scale. Nonetheless, we reinforce that what is provided here is not a ‘solution’ to a decision
problem, but an input. We have not attempted to balance costs, constraints, social preferences and
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the myriad of other considerations that necessarily go into complex land management and planning
decisions.
A significant part of this assessment has been the mapping and modelling of the distributions of
566 species and communities within the LHSA region. While modelled distributions always have their
limitations, we note that the species distribution models produced in this project had high to very
high predictive performance. Therefore, we feel confident that they provide significant improvement
to our understanding of biodiversity patterns within the LHSA region, and represent the best
available distribution data for inclusion in the spatial conservation prioritisation. However, as a
general rule, we recommend that sites highlighted by subsequent analyses as high priority for
conservation and at risk from development, and areas thought to be of high conservation value that
were not identified as such in our analysis be surveyed as part of the decision-making process.
Development proposals that are likely to result in the loss of biodiversity features or severely
decrease their current distribution size should be analysed in further detail to understand where
mitigation actions could provide substantial conservation benefit. In order to avoid further loss in
local biodiversity, known locations of highly threatened species should be excluded from
development. We also recommend that proposed development areas overlapping with the high
priority conservation areas should be targeted for further surveys to validate the anticipated losses
and to further guide appropriate actions for solving these conflicts. Areas included in the top 5% and
10% priorities are particularly difficult, or impossible, to replace with sites elsewhere in the LHSA
region and should, therefore, be the core focus of any further assessments.
We re-iterate that the conservation priorities identified in this assessment are based on local
habitat quality and representation of the species and communities included in the spatial
prioritisation. These areas have not been selected from the perspective of ecological processes and,
therefore, the outputs of the spatial prioritisation do not provide any information about the
adequacy of the solution to ensure the long term persistence of biodiversity features within the
landscape. Such evaluations require more detailed information on species demographics and
movement, which is rarely available for large number of species. While consideration of potential
connectivity requirements between habitat patches and the potential impacts of similar
development scenarios in the LHSA region has been assessed by Lechner and Lefroy (2014), these
were done based on average dispersal distances across a range of species and therefore, may not be
representative of connectivity requirements for individual biodiversity features. However,
maintaining healthy ecosystems starts by securing the presence of their biodiversity components,
followed by actions aimed to preserve or improve the underlining intra- and interspecific dynamics.
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For a small subset of well-studied species it may be possible to develop spatially-explicit
metapopulation models to evaluate their likely long-term persistence under different development
scenarios. This could be an appropriate next step to help decision makers understand the potential
implications of the proposed development and the additional conservation investments needed to
secure critical long-term ecological processes. Such models should ideally also consider threatening
processes outside the LHSA that may influence the persistence of the species of interest. Although it
was not possible to include ecological processes in this assessment, the prioritisation provides
valuable information on the critical habitat needed to halt further biodiversity loss in the LHSA. The
priority maps could also be used to guide future biodiversity mapping exercises, particularly in areas
that are currently poorly surveyed, as the underlining species distribution models have the potential
to reveal locations of previously unknown local populations or important habitats (Guisan & Thuiller
2005).
We note that many small habitat patches were identified as having high conservation value.
Many small, isolated fragments of vegetation on the river valleys of Lower Hunter that have been
identified as high priority conservation areas because they contain the last remaining occurrences of
biodiversity features that are not found elsewhere in the landscape − essentially they are
irreplaceable. Given the paucity of evidence supporting the notion that these patches are unviable
simply because they are small, we strongly recommend considering these patches irreplaceable until
it can be shown that the conservation values they contain can be better secured elsewhere.
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List of Appendices
Appendix 1. Individual results for species distributions modelled using Maxent.
Appendix 2. Description of the prioritisation algorithm.
Appendix 3. List of biodiversity features included in Zonation analyses.
Appendix 4. Summary of the spatial data used to create the protected area layer used in
the Zonation analyses.
Appendix 5. The proportion of each biodiversity feature’s LHSA distribution that is
represented in the high priority conservation areas and is currently
protected.
Appendix 6. Spatial layers used to create development scenario footprints.
Appendix 7. Proportion of each biodiversity feature’s LHSA distribution predicted to be
lost under different development scenario.
Appendix 8. Overlap of proposed future development plans and conservation priority
areas within the LHSA region.
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